Test_Pra_Interview_DA_Botika.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/farizalmustaqim/Proyek-Sentiment-Analysis-NLP/blob/main/Test_Pra_Interview_DA_Botika.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __Proyek Sentiment Analysis NLP__\n",
        "Nama: Fariz Al Mustaqim\n"
      ],
      "metadata": {
        "id": "RXD8MklOUsMY"
      },
      "id": "RXD8MklOUsMY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __1. Pelabelan Dataset__"
      ],
      "metadata": {
        "id": "rYk0S0fXVdUx"
      },
      "id": "rYk0S0fXVdUx"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pelabelan dataset teks menggunakan sentimen analisis dari packages transformers"
      ],
      "metadata": {
        "id": "4xlxgnFcXU2s"
      },
      "id": "4xlxgnFcXU2s"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AUjuWGTw28am",
        "outputId": "75abbe96-bb35-44c9-b8c9-5e52a7230477"
      },
      "id": "AUjuWGTw28am",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.20.1-py3-none-any.whl (4.4 MB)\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.1)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.8.1-py3-none-any.whl (101 kB)\n",
            "\u001b[K     |████████████████████████████████| 101 kB 9.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 58.3 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 38.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.8.1 pyyaml-6.0 tokenizers-0.12.1 transformers-4.20.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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      "id": "437913c4",
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        "id": "437913c4",
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at indolem/indobertweet-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModel\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"indolem/indobertweet-base-uncased\")\n",
        "model = AutoModel.from_pretrained(\"indolem/indobertweet-base-uncased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3fb82bd9",
      "metadata": {
        "id": "3fb82bd9",
        "outputId": "72c1c305-48aa-4119-9d07-fdbea01519c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertModel(\n",
              "  (embeddings): BertEmbeddings(\n",
              "    (word_embeddings): Embedding(31923, 768, padding_idx=0)\n",
              "    (position_embeddings): Embedding(512, 768)\n",
              "    (token_type_embeddings): Embedding(2, 768)\n",
              "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (encoder): BertEncoder(\n",
              "    (layer): ModuleList(\n",
              "      (0): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (2): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (3): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (4): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (5): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (6): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (7): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (8): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (9): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (10): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (11): BertLayer(\n",
              "        (attention): BertAttention(\n",
              "          (self): BertSelfAttention(\n",
              "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (output): BertSelfOutput(\n",
              "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (intermediate): BertIntermediate(\n",
              "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          (intermediate_act_fn): GELUActivation()\n",
              "        )\n",
              "        (output): BertOutput(\n",
              "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pooler): BertPooler(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (activation): Tanh()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ca48e94",
      "metadata": {
        "id": "5ca48e94",
        "outputId": "5bb5c77b-1129-41d4-8db2-d9353b9992b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258,
          "referenced_widgets": [
            "2e393ab28e2f4120be07c9938b2e1ebb",
            "443b5051582b46699af09f540b29b36b",
            "4716f4ecc45a4344a8a4525ef0f55677",
            "1f3bfed1071c4228aa359f2a70ad36af",
            "96f5a68a60f24081aebd475227ca8c09",
            "fa52df1b7f664f2fb666604cfefa5887",
            "93fd6e92eef94379868cb9b7e22da90a",
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            "cc5f038b9e0d4fddaa765e62a3fde32b",
            "cb817575732a41baba41183fe041c557",
            "c8b812cf670c4eff8511c46074a3f222",
            "a192050163084870b335925e05449d3e",
            "a0cc6d425e324944813feaf21c65cfea",
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            "c5739de0b3cc4e04b6555c1bc026939a",
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            "178e970085e04dbb8eb1f6e701620042",
            "e391482c379c4a81967b2f98001d886d",
            "f9284d0fcf5648b5a8a723137d2af77a",
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            "eb20e935aa664c09a10e36a297ec0415",
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            "8843d0b19c244b74b1bd22101baf6c34",
            "18924776c3f745be879dd3942bcc6231",
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            "7948e14109de4c6880fdae13e29c2bf9",
            "39a8441e43924578946b08678ebc00af"
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        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/932 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e393ab28e2f4120be07c9938b2e1ebb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/476M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a192050163084870b335925e05449d3e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/328 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c5739de0b3cc4e04b6555c1bc026939a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/789k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5baed0a60b0443689a47846bdee846aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/505k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ed4a39e0a714a188057dd64b9478f75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.31M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "18924776c3f745be879dd3942bcc6231"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7b718bec0854745831b05ce55b90c7f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'negative', 'score': 0.9990731477737427}]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "pretrained_name = \"sahri/indonesiasentiment\"\n",
        "\n",
        "nlp = pipeline(\n",
        "    \"sentiment-analysis\",\n",
        "    model=pretrained_name,\n",
        "    tokenizer=pretrained_name\n",
        ")\n",
        "\n",
        "nlp(\"Saya udah gak ngerti.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00d57c05",
      "metadata": {
        "id": "00d57c05",
        "outputId": "c2cb6330-7af4-466b-b5d5-ae0e3b6dbc0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'positive', 'score': 0.9978877902030945}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "nlp(\"Makasih\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c43eeb3",
      "metadata": {
        "id": "1c43eeb3",
        "outputId": "72a82243-4deb-486d-b24a-d2f739046c4e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'negative', 'score': 0.9985276460647583}]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "nlp(\"jelek\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "167eeb7d",
      "metadata": {
        "id": "167eeb7d",
        "outputId": "e7e7fee1-10e8-466b-f79a-99718e6273ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'neutral', 'score': 0.9958090782165527}]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "nlp(\"rakyatnya ngantri minyak goreng\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Mengimport Dataset** "
      ],
      "metadata": {
        "id": "ciEVxhlt4xrd"
      },
      "id": "ciEVxhlt4xrd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd405dad",
      "metadata": {
        "id": "fd405dad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "5c7a04f8-775c-486f-9522-260e6906f27f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 data teks\n",
              "0                             Makasii ????\n",
              "1                  Sekarang jam berapa ya?\n",
              "2     Hp gua udah 4g gausah diaktifin lagi\n",
              "3                                 Semlekom\n",
              "4    Sejag kapan nama saiia blue (Har Har)\n",
              "..                                     ...\n",
              "378      Punya anjir. Bacod mulu dari tadi\n",
              "379          Terima kasih atas infonya bak\n",
              "380                Can you speak English ?\n",
              "381                         Namanya siapa?\n",
              "382                   Mana gua tau selesai\n",
              "\n",
              "[383 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ed6486d-e953-4a5c-af84-74e6d1ec7d52\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>data teks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Makasii ????</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sekarang jam berapa ya?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hp gua udah 4g gausah diaktifin lagi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Semlekom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sejag kapan nama saiia blue (Har Har)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378</th>\n",
              "      <td>Punya anjir. Bacod mulu dari tadi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379</th>\n",
              "      <td>Terima kasih atas infonya bak</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>Can you speak English ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>Namanya siapa?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>Mana gua tau selesai</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>383 rows × 1 columns</p>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/farizalmustaqim/Test-Pra-Interview-DA-Botika/main/Data%20Chat%20User%20ke%20Bot%20(1).csv', on_bad_lines='skip')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d3b0ac65",
      "metadata": {
        "id": "d3b0ac65",
        "outputId": "4a98a149-3dc6-46a6-8a1f-d63cb1775c44",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
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          "metadata": {},
          "execution_count": 9
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      ],
      "source": [
        "#Memasukan label pada Dataset\n",
        "df1 = nlp(df['data teks'].tolist())\n",
        "df1\n"
      ]
    },
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              "        label     score\n",
              "0    negative  0.994079\n",
              "1    negative  0.994968\n",
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          "metadata": {},
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      ],
      "source": [
        "# Membuat pandas Dataframe untuk fitur label dan score\n",
        "df2= pd.DataFrame(df1)\n",
        "df2"
      ]
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      "execution_count": null,
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              "                                 data teks     label     score\n",
              "0                             Makasii ????  negative  0.994079\n",
              "1                  Sekarang jam berapa ya?  negative  0.994968\n",
              "2     Hp gua udah 4g gausah diaktifin lagi  negative  0.998051\n",
              "3                                 Semlekom  negative  0.991702\n",
              "4    Sejag kapan nama saiia blue (Har Har)   neutral  0.989627\n",
              "..                                     ...       ...       ...\n",
              "378      Punya anjir. Bacod mulu dari tadi  negative  0.944670\n",
              "379          Terima kasih atas infonya bak  positive  0.965316\n",
              "380                Can you speak English ?  negative  0.997171\n",
              "381                         Namanya siapa?  negative  0.893093\n",
              "382                   Mana gua tau selesai  negative  0.997722\n",
              "\n",
              "[383 rows x 3 columns]"
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              "      <td>Sejag kapan nama saiia blue (Har Har)</td>\n",
              "      <td>neutral</td>\n",
              "      <td>0.989627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378</th>\n",
              "      <td>Punya anjir. Bacod mulu dari tadi</td>\n",
              "      <td>negative</td>\n",
              "      <td>0.944670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379</th>\n",
              "      <td>Terima kasih atas infonya bak</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.965316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>Can you speak English ?</td>\n",
              "      <td>negative</td>\n",
              "      <td>0.997171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>Namanya siapa?</td>\n",
              "      <td>negative</td>\n",
              "      <td>0.893093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>Mana gua tau selesai</td>\n",
              "      <td>negative</td>\n",
              "      <td>0.997722</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>383 rows × 3 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Menggabungkan Dataframe fitur/column data teks, label dan score\n",
        "df3 = pd.concat([df,df2],axis=1)\n",
        "df3"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Sudah didapat hasil klasifikasi dari data teks**"
      ],
      "metadata": {
        "id": "sEj1e13yXh4g"
      },
      "id": "sEj1e13yXh4g"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5911ffe2",
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        "colab": {
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          "height": 424
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        "outputId": "84092276-3959-4c82-b84a-bb22bdd54611"
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        {
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            "text/plain": [
              "                                 data teks     label\n",
              "0                             Makasii ????  negative\n",
              "1                  Sekarang jam berapa ya?  negative\n",
              "2     Hp gua udah 4g gausah diaktifin lagi  negative\n",
              "3                                 Semlekom  negative\n",
              "4    Sejag kapan nama saiia blue (Har Har)   neutral\n",
              "..                                     ...       ...\n",
              "378      Punya anjir. Bacod mulu dari tadi  negative\n",
              "379          Terima kasih atas infonya bak  positive\n",
              "380                Can you speak English ?  negative\n",
              "381                         Namanya siapa?  negative\n",
              "382                   Mana gua tau selesai  negative\n",
              "\n",
              "[383 rows x 2 columns]"
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              "      <th>379</th>\n",
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              "      <td>positive</td>\n",
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              "      <td>Can you speak English ?</td>\n",
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              "      <td>Namanya siapa?</td>\n",
              "      <td>negative</td>\n",
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              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>Mana gua tau selesai</td>\n",
              "      <td>negative</td>\n",
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              "          const element = document.querySelector('#df-948be934-4509-464a-8d21-2bedaf7b076b');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df_klasifikasi = df3.drop(columns = 'score')\n",
        "df_klasifikasi"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __2. Preprocessing data__"
      ],
      "metadata": {
        "id": "iMd3lZI3Xfsb"
      },
      "id": "iMd3lZI3Xfsb"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menggunakan proses preprocessing data seperti berikut ini:\n",
        "1. Case Folding\n",
        "2. Filtering\n",
        "3. Stopword\n",
        "4. Stemming"
      ],
      "metadata": {
        "id": "18ybDlbuY3cs"
      },
      "id": "18ybDlbuY3cs"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menginstal library sastrawi untuk preprocessing NLP bahasa Indonesia"
      ],
      "metadata": {
        "id": "_TYfvZKc3bSI"
      },
      "id": "_TYfvZKc3bSI"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip -q install sastrawi"
      ],
      "metadata": {
        "id": "JLEsbatnYn4b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a794776f-8f65-47a9-a335-2db003d2c3b4"
      },
      "id": "JLEsbatnYn4b",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |█▋                              | 10 kB 22.5 MB/s eta 0:00:01\r\u001b[K     |███▏                            | 20 kB 22.5 MB/s eta 0:00:01\r\u001b[K     |████▊                           | 30 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████▎                         | 40 kB 10.6 MB/s eta 0:00:01\r\u001b[K     |███████▉                        | 51 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |█████████▍                      | 61 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 71 kB 5.5 MB/s eta 0:00:01\r\u001b[K     |████████████▌                   | 81 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 92 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 102 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████▏              | 112 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████▊             | 122 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 133 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▉          | 143 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▍        | 153 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 163 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▋     | 174 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▏   | 184 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▊  | 194 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 204 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 209 kB 5.3 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download(\"stopwords\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLfAMd3GY1pJ",
        "outputId": "f4d43e28-fd86-45e9-8a05-ef16f4e3e0f5"
      },
      "id": "hLfAMd3GY1pJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7003fbe5",
      "metadata": {
        "id": "7003fbe5"
      },
      "outputs": [],
      "source": [
        "#import library yang dibutuhkan\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_klasifikasi.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "i6aq11YmZEFl",
        "outputId": "ad3ec2e4-5d37-425b-ecc3-742caf1f401d"
      },
      "id": "i6aq11YmZEFl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               data teks     label\n",
              "0                           Makasii ????  negative\n",
              "1                Sekarang jam berapa ya?  negative\n",
              "2   Hp gua udah 4g gausah diaktifin lagi  negative\n",
              "3                               Semlekom  negative\n",
              "4  Sejag kapan nama saiia blue (Har Har)   neutral"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sekarang jam berapa ya?</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hp gua udah 4g gausah diaktifin lagi</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Semlekom</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sejag kapan nama saiia blue (Har Har)</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-833fe993-7b0a-4e1c-944a-d5118400c041')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-833fe993-7b0a-4e1c-944a-d5118400c041 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-833fe993-7b0a-4e1c-944a-d5118400c041');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_klasifikasi.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQubWvHlZeID",
        "outputId": "60abb312-08c3-4b43-c0c5-05a4015221bb"
      },
      "id": "IQubWvHlZeID",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 383 entries, 0 to 382\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   data teks  383 non-null    object\n",
            " 1   label      383 non-null    object\n",
            "dtypes: object(2)\n",
            "memory usage: 6.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## __2.1 Case Folding__"
      ],
      "metadata": {
        "id": "Y2pUXgKnaR86"
      },
      "id": "Y2pUXgKnaR86"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Case Folding digunakan untuk bisa memgubah kata/kalimat menjadi lowecase, menghapus angka dan tanda baca, menghapus whitespace (karakter kosong) yang mana tidak perlukan dalam pemodelan NLP"
      ],
      "metadata": {
        "id": "dYy6H7j86uYW"
      },
      "id": "dYy6H7j86uYW"
    },
    {
      "cell_type": "code",
      "source": [
        "# Import library regex\n",
        "import re\n",
        "\n",
        "# Buat fungction langkah dari case folding dan filtering\n",
        "def caseFolding(text):\n",
        "  text = text.lower() # Mengubah teks menjadi lower case\n",
        "  text = re.sub(r'https?://\\S+|www\\.\\S+', '', text) # Menghapus URL\n",
        "  text = re.sub(r'[0-9]+', '', text) # Menghapus angka \n",
        "  text = re.sub(r'[^\\w\\s]', '', text) # Menghapus karakter tanda baca\n",
        "  text = text.strip()\n",
        "  return text"
      ],
      "metadata": {
        "id": "7-b88mXCZjr5"
      },
      "id": "7-b88mXCZjr5",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_sample = df_klasifikasi['data teks'].iloc[381]\n",
        "case_folding = caseFolding(raw_sample)"
      ],
      "metadata": {
        "id": "j3l3uWaaagJj"
      },
      "id": "j3l3uWaaagJj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Raw data \\t:', raw_sample)\n",
        "print('Case folding \\t', case_folding)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9BJ5KPFFasFx",
        "outputId": "48f776ba-9ddb-4e2a-b3da-0699664636d8"
      },
      "id": "9BJ5KPFFasFx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Raw data \t: Namanya siapa?\n",
            "Case folding \t namanya siapa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## __2.2 Word Normalization__"
      ],
      "metadata": {
        "id": "mrQNn61rfNpo"
      },
      "id": "mrQNn61rfNpo"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Word Normalization digunakan untuk mengubah kata/kalimat slang menjadi seperti kata bakunya."
      ],
      "metadata": {
        "id": "501pOzSb7Igp"
      },
      "id": "501pOzSb7Igp"
    },
    {
      "cell_type": "code",
      "source": [
        "# Download corpus slang word\n",
        "\n",
        "!wget https://raw.githubusercontent.com/ksnugroho/klasifikasi-spam-sms/master/data/key_norm.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVtEZqJhfQGa",
        "outputId": "1652d441-d728-4e2d-e6a2-2464c6deeed5"
      },
      "id": "UVtEZqJhfQGa",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-07-12 06:11:21--  https://raw.githubusercontent.com/ksnugroho/klasifikasi-spam-sms/master/data/key_norm.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 80969 (79K) [text/plain]\n",
            "Saving to: ‘key_norm.csv’\n",
            "\n",
            "key_norm.csv        100%[===================>]  79.07K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2022-07-12 06:11:21 (5.83 MB/s) - ‘key_norm.csv’ saved [80969/80969]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Meload data key_norm.csv\n",
        "key_norm = pd.read_csv('/content/key_norm.csv')\n",
        "\n",
        "def text_normalize(text):\n",
        "  text = ' '.join([key_norm[key_norm['singkat']==word]['hasil'].values[0] if (key_norm['singkat']==word).any() else word for word in text.split()])\n",
        "  text = str.lower(text)\n",
        "  return text\n"
      ],
      "metadata": {
        "id": "kDOB037rg-D9"
      },
      "id": "kDOB037rg-D9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_sample = df_klasifikasi['data teks'].iloc[381]\n",
        "case_folding = caseFolding(raw_sample)\n",
        "normalize = text_normalize(case_folding)"
      ],
      "metadata": {
        "id": "Bp4Tk41H3vlC"
      },
      "id": "Bp4Tk41H3vlC",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Raw data \\t:', raw_sample)\n",
        "print('Case folding \\t', case_folding)\n",
        "print('text_normalize \\t', normalize)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iu3v-a0p3zjd",
        "outputId": "e3447d5f-3489-4fa5-bdbf-46905e459ad6"
      },
      "id": "Iu3v-a0p3zjd",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Raw data \t: Namanya siapa?\n",
            "Case folding \t namanya siapa\n",
            "text_normalize \t namanya siapa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## __2.3 Stopword Removal__"
      ],
      "metadata": {
        "id": "mQh_TDd2hEFO"
      },
      "id": "mQh_TDd2hEFO"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stopword Removal (Filtering) adalah tahap mengambil kata-kata penting dari hasil token dengan menggunakan algoritma stoplist (membuang kata kurang penting) atau wordlist (menyimpan kata penting)."
      ],
      "metadata": {
        "id": "ZWqjpceF7Wp1"
      },
      "id": "ZWqjpceF7Wp1"
    },
    {
      "cell_type": "code",
      "source": [
        "# Import library stopword\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "stopwords_ind = stopwords.words('indonesian')"
      ],
      "metadata": {
        "id": "WXLURAishFn2"
      },
      "id": "WXLURAishFn2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(stopwords_ind)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrQeE1sWhJDC",
        "outputId": "3eba418c-ac28-407a-d562-a13195b8293b"
      },
      "id": "mrQeE1sWhJDC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "758"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stopword Indonesia"
      ],
      "metadata": {
        "id": "zz-hl3bBhNqv"
      },
      "id": "zz-hl3bBhNqv"
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat fungsi stopword removal\n",
        "\n",
        "stopwords_ind = stopwords_ind\n",
        "\n",
        "def remove_stop_word(text):\n",
        "  clean_word = []\n",
        "  text = text.split()\n",
        "  for word in text:\n",
        "    if word not in stopwords_ind:\n",
        "      clean_word.append(word)\n",
        "  return \" \".join(clean_word)\n"
      ],
      "metadata": {
        "id": "R1J16a5fhLP3"
      },
      "id": "R1J16a5fhLP3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_sample = df_klasifikasi['data teks'].iloc[381]\n",
        "case_folding = caseFolding(raw_sample)\n",
        "normalize = text_normalize(case_folding)\n",
        "stopword_removal = remove_stop_word(normalize)"
      ],
      "metadata": {
        "id": "F-ZI6-TKht9f"
      },
      "id": "F-ZI6-TKht9f",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Raw data \\t:', raw_sample)\n",
        "print('Case folding \\t', case_folding)\n",
        "print('text_normalize \\t', normalize)\n",
        "print('Stop word \\t', stopword_removal)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dc9E18GNh4yb",
        "outputId": "dbb12338-621a-4938-a4de-4e9af521bf74"
      },
      "id": "dc9E18GNh4yb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Raw data \t: Namanya siapa?\n",
            "Case folding \t namanya siapa\n",
            "text_normalize \t namanya siapa\n",
            "Stop word \t namanya\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## __2.4 Stemming__"
      ],
      "metadata": {
        "id": "noDGm5Y6io7B"
      },
      "id": "noDGm5Y6io7B"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stemming adalah proses Mengubah kata ke bentuk dasarnya, namun bentuk dasar tersebut tidak berarti sama dengan akar kata (root word)."
      ],
      "metadata": {
        "id": "3Xb4IeaP7lFC"
      },
      "id": "3Xb4IeaP7lFC"
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.text import TextCollection\n",
        "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
        "\n",
        "factory = StemmerFactory()\n",
        "stemmer = factory.create_stemmer()\n",
        "\n",
        "#Di sini kita buat fungsi stemming\n",
        "\n",
        "def stemming(text):\n",
        "  text = stemmer.stem(text)\n",
        "  return text\n"
      ],
      "metadata": {
        "id": "uO3vDmV3iqEz"
      },
      "id": "uO3vDmV3iqEz",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_sample = df_klasifikasi['data teks'].iloc[381]\n",
        "case_folding = caseFolding(raw_sample)\n",
        "normalize = text_normalize(case_folding)\n",
        "stopword_removal = remove_stop_word(normalize)\n",
        "text_stemming = stemming(stopword_removal)\n"
      ],
      "metadata": {
        "id": "BpG-qo8njK9Q"
      },
      "id": "BpG-qo8njK9Q",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print ('Raw data \\t:',raw_sample)\n",
        "print ('Case folding \\t:', case_folding)\n",
        "print ('text_normalize \\t:', normalize)\n",
        "print ('Stop word \\t:', stopword_removal)\n",
        "print ('Stemming \\t:', text_stemming)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PRgQhfxjM6k",
        "outputId": "e7f9da2f-21bb-4e7c-c162-3762053f3a5a"
      },
      "id": "7PRgQhfxjM6k",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Raw data \t: Namanya siapa?\n",
            "Case folding \t: namanya siapa\n",
            "text_normalize \t: namanya siapa\n",
            "Stop word \t: namanya\n",
            "Stemming \t: nama\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## __2.5 Pre-processing Pipeline__"
      ],
      "metadata": {
        "id": "JI7s63yUjZ-S"
      },
      "id": "JI7s63yUjZ-S"
    },
    {
      "cell_type": "code",
      "source": [
        "# Disini kita buat pipelinenya (menggabungkan semua fungsi di atas)\n",
        "\n",
        "def text_preprocessing(text):\n",
        "  text = caseFolding(text)\n",
        "  text = text_normalize(text)\n",
        "  text = remove_stop_word(text)\n",
        "  text = stemming(text)\n",
        "  return text\n"
      ],
      "metadata": {
        "id": "F8oceRLQjlHM"
      },
      "id": "F8oceRLQjlHM",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Time untuk mendapatkan waktu komputasi\n",
        "%%time \n",
        "#proses menjalankan fungsi text_preprocessing pada data teks\n",
        "df_klasifikasi['clean data teks'] = df_klasifikasi['data teks'].apply(text_preprocessing)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98Ik5pv8kTuc",
        "outputId": "28ad15ea-1586-4f3f-aecb-21b6bc7d2ce0"
      },
      "id": "98Ik5pv8kTuc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 36.8 s, sys: 153 ms, total: 36.9 s\n",
            "Wall time: 45.6 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Simpan data , jika anda ingin menggunakan data clean_teks\n",
        "df_klasifikasi.to_csv('clean data teks.csv')"
      ],
      "metadata": {
        "id": "k3VHVgwWlQ-5"
      },
      "id": "k3VHVgwWlQ-5",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_klasifikasi.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BFSzuE4IlXsF",
        "outputId": "d27b6c7e-e096-4332-9373-b015c3bc07b6"
      },
      "id": "BFSzuE4IlXsF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             data teks     label             clean data teks\n",
              "378  Punya anjir. Bacod mulu dari tadi  negative            anjir bacod mulu\n",
              "379      Terima kasih atas infonya bak  positive  terima kasih informasi bak\n",
              "380            Can you speak English ?  negative               speak english\n",
              "381                     Namanya siapa?  negative                        nama\n",
              "382               Mana gua tau selesai  negative                 tau selesai"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>data teks</th>\n",
              "      <th>label</th>\n",
              "      <th>clean data teks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>378</th>\n",
              "      <td>Punya anjir. Bacod mulu dari tadi</td>\n",
              "      <td>negative</td>\n",
              "      <td>anjir bacod mulu</td>\n",
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              "    <tr>\n",
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              "      <td>Terima kasih atas infonya bak</td>\n",
              "      <td>positive</td>\n",
              "      <td>terima kasih informasi bak</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>Can you speak English ?</td>\n",
              "      <td>negative</td>\n",
              "      <td>speak english</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>Namanya siapa?</td>\n",
              "      <td>negative</td>\n",
              "      <td>nama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <td>Mana gua tau selesai</td>\n",
              "      <td>negative</td>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __3. Feature Engineering / Feature Extraction__"
      ],
      "metadata": {
        "id": "P1hknrMpq8af"
      },
      "id": "P1hknrMpq8af"
    },
    {
      "cell_type": "code",
      "source": [
        "# Pisahkan kolom feature dan target\n",
        "\n",
        "X = df_klasifikasi['clean data teks']\n",
        "y = df_klasifikasi['label']"
      ],
      "metadata": {
        "id": "gMEpF1lKskRy"
      },
      "id": "gMEpF1lKskRy",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature pada variabel X\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukzscIiptgKH",
        "outputId": "76c94bd8-a1b1-4290-f638-67733e49678a"
      },
      "id": "ukzscIiptgKH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                            makasii\n",
              "1                             jam ya\n",
              "2                       hp diaktifin\n",
              "3                           semlekom\n",
              "4      sejag nama saiia blue har har\n",
              "                   ...              \n",
              "378                 anjir bacod mulu\n",
              "379       terima kasih informasi bak\n",
              "380                    speak english\n",
              "381                             nama\n",
              "382                      tau selesai\n",
              "Name: clean data teks, Length: 383, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# target pada variabel y\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYQ8FxwVtiOc",
        "outputId": "03abccbb-bf51-4df3-9e3e-66d7e250b07e"
      },
      "id": "BYQ8FxwVtiOc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      negative\n",
              "1      negative\n",
              "2      negative\n",
              "3      negative\n",
              "4       neutral\n",
              "         ...   \n",
              "378    negative\n",
              "379    positive\n",
              "380    negative\n",
              "381    negative\n",
              "382    negative\n",
              "Name: label, Length: 383, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## __3.1 Feature Extraction (TF-ID)__"
      ],
      "metadata": {
        "id": "IlWUF7wiuQzy"
      },
      "id": "IlWUF7wiuQzy"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "#Unigram\n",
        "tf_idf = TfidfVectorizer(ngram_range=(1,1))\n",
        "tf_idf.fit(X)\n",
        "\n",
        "X_tf_idf = tf_idf.transform(X)\n"
      ],
      "metadata": {
        "id": "lWwW3N6auSs0"
      },
      "id": "lWwW3N6auSs0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Menampilkan vocabulary dari TF-IDF\n",
        "tf_idf.vocabulary_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsleffJPcQqH",
        "outputId": "34da9474-39b8-4bb5-c00e-5ceee35b16e0"
      },
      "id": "AsleffJPcQqH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ach': 0,\n",
              " 'aduh': 1,\n",
              " 'aduuhh': 2,\n",
              " 'ae': 3,\n",
              " 'ah': 4,\n",
              " 'ahh': 5,\n",
              " 'alaah': 6,\n",
              " 'alhamdullilah': 7,\n",
              " 'allaikum': 8,\n",
              " 'am': 9,\n",
              " 'aman': 10,\n",
              " 'anjay': 11,\n",
              " 'anjeng': 12,\n",
              " 'anjir': 13,\n",
              " 'anjirrr': 14,\n",
              " 'anying': 15,\n",
              " 'apa': 16,\n",
              " 'apaansihgt': 17,\n",
              " 'apasih': 18,\n",
              " 'arigato': 19,\n",
              " 'assalamu': 20,\n",
              " 'assalamualaiku': 21,\n",
              " 'assalamualaikumin': 22,\n",
              " 'asslamualaikum': 23,\n",
              " 'auk': 24,\n",
              " 'auw': 25,\n",
              " 'ayam': 26,\n",
              " 'bacod': 27,\n",
              " 'bae': 28,\n",
              " 'bagus': 29,\n",
              " 'bahasa': 30,\n",
              " 'bak': 31,\n",
              " 'balas': 32,\n",
              " 'bang': 33,\n",
              " 'banget': 34,\n",
              " 'bantu': 35,\n",
              " 'barusan': 36,\n",
              " 'beli': 37,\n",
              " 'belit': 38,\n",
              " 'bener': 39,\n",
              " 'bentar': 40,\n",
              " 'berhentiin': 41,\n",
              " 'besok': 42,\n",
              " 'bgni': 43,\n",
              " 'bgus': 44,\n",
              " 'bicara': 45,\n",
              " 'bikin': 46,\n",
              " 'bilang': 47,\n",
              " 'bingung': 48,\n",
              " 'blue': 49,\n",
              " 'bngt': 50,\n",
              " 'bnykn': 51,\n",
              " 'bobo': 52,\n",
              " 'bodoamayttt': 53,\n",
              " 'bodoh': 54,\n",
              " 'bom': 55,\n",
              " 'book': 56,\n",
              " 'boot': 57,\n",
              " 'bosan': 58,\n",
              " 'bosen': 59,\n",
              " 'bot': 60,\n",
              " 'break': 61,\n",
              " 'butuh': 62,\n",
              " 'bye': 63,\n",
              " 'cai': 64,\n",
              " 'cantik': 65,\n",
              " 'capek': 66,\n",
              " 'cek': 67,\n",
              " 'cepat': 68,\n",
              " 'chat': 69,\n",
              " 'cinta': 70,\n",
              " 'clear': 71,\n",
              " 'complient': 72,\n",
              " 'cot': 73,\n",
              " 'coyyy': 74,\n",
              " 'cry': 75,\n",
              " 'cukk': 76,\n",
              " 'dahh': 77,\n",
              " 'dasar': 78,\n",
              " 'deh': 79,\n",
              " 'dehh': 80,\n",
              " 'dengar': 81,\n",
              " 'detaya': 82,\n",
              " 'diaktifin': 83,\n",
              " 'diam': 84,\n",
              " 'donk': 85,\n",
              " 'doyan': 86,\n",
              " 'dude': 87,\n",
              " 'dukung': 88,\n",
              " 'eh': 89,\n",
              " 'english': 90,\n",
              " 'erti': 91,\n",
              " 'euy': 92,\n",
              " 'fa': 93,\n",
              " 'full': 94,\n",
              " 'gai': 95,\n",
              " 'genit': 96,\n",
              " 'gila': 97,\n",
              " 'go': 98,\n",
              " 'guna': 99,\n",
              " 'hadeeeeuh': 100,\n",
              " 'hadeuhh': 101,\n",
              " 'hahahahhahahahaha': 102,\n",
              " 'halo': 103,\n",
              " 'halon': 104,\n",
              " 'har': 105,\n",
              " 'hei': 106,\n",
              " 'herat': 107,\n",
              " 'hhhhhh': 108,\n",
              " 'hi': 109,\n",
              " 'hii': 110,\n",
              " 'hm': 111,\n",
              " 'hmmm': 112,\n",
              " 'hobby': 113,\n",
              " 'hobi': 114,\n",
              " 'hole': 115,\n",
              " 'hp': 116,\n",
              " 'hu': 117,\n",
              " 'hy': 118,\n",
              " 'if': 119,\n",
              " 'ihhh': 120,\n",
              " 'illu': 121,\n",
              " 'informasi': 122,\n",
              " 'internet': 123,\n",
              " 'isi': 124,\n",
              " 'istri': 125,\n",
              " 'it': 126,\n",
              " 'jam': 127,\n",
              " 'jaring': 128,\n",
              " 'jawabanya': 129,\n",
              " 'jelek': 130,\n",
              " 'jjkakak': 131,\n",
              " 'jomblo': 132,\n",
              " 'jumpa': 133,\n",
              " 'ka': 134,\n",
              " 'kabar': 135,\n",
              " 'kak': 136,\n",
              " 'kakak': 137,\n",
              " 'kali': 138,\n",
              " 'kasih': 139,\n",
              " 'kau': 140,\n",
              " 'kay': 141,\n",
              " 'kecewa': 142,\n",
              " 'keluh': 143,\n",
              " 'kerja': 144,\n",
              " 'kesel': 145,\n",
              " 'klik': 146,\n",
              " 'ko': 147,\n",
              " 'kog': 148,\n",
              " 'kokapus': 149,\n",
              " 'komplain': 150,\n",
              " 'komputer': 151,\n",
              " 'ku': 152,\n",
              " 'kuya': 153,\n",
              " 'kwkwkkw': 154,\n",
              " 'kwkwkwkw': 155,\n",
              " 'kwlwkwkwkkwkwkw': 156,\n",
              " 'lahir': 157,\n",
              " 'layan': 158,\n",
              " 'left': 159,\n",
              " 'logout': 160,\n",
              " 'loh': 161,\n",
              " 'losing': 162,\n",
              " 'lot': 163,\n",
              " 'love': 164,\n",
              " 'lumayan': 165,\n",
              " 'maaf': 166,\n",
              " 'mahh': 167,\n",
              " 'makan': 168,\n",
              " 'makasii': 169,\n",
              " 'makasiih': 170,\n",
              " 'makasiiihh': 171,\n",
              " 'maksud': 172,\n",
              " 'malam': 173,\n",
              " 'malas': 174,\n",
              " 'mam': 175,\n",
              " 'mana': 176,\n",
              " 'mas': 177,\n",
              " 'masmba': 178,\n",
              " 'masntap': 179,\n",
              " 'may': 180,\n",
              " 'maya': 181,\n",
              " 'mbak': 182,\n",
              " 'mending': 183,\n",
              " 'menu': 184,\n",
              " 'menyamoaikan': 185,\n",
              " 'meong': 186,\n",
              " 'mi': 187,\n",
              " 'milih': 188,\n",
              " 'min': 189,\n",
              " 'misi': 190,\n",
              " 'mmm': 191,\n",
              " 'mohon': 192,\n",
              " 'mucul': 193,\n",
              " 'mulu': 194,\n",
              " 'muncul': 195,\n",
              " 'nama': 196,\n",
              " 'namalu': 197,\n",
              " 'nan': 198,\n",
              " 'ngalem': 199,\n",
              " 'ngapain': 200,\n",
              " 'ngerti': 201,\n",
              " 'ngeseli': 202,\n",
              " 'ngomongnya': 203,\n",
              " 'nih': 204,\n",
              " 'niih': 205,\n",
              " 'njit': 206,\n",
              " 'nnti': 207,\n",
              " 'nnya': 208,\n",
              " 'nomor': 209,\n",
              " 'nuw': 210,\n",
              " 'nya': 211,\n",
              " 'nyaman': 212,\n",
              " 'nyambung': 213,\n",
              " 'oh': 214,\n",
              " 'ok': 215,\n",
              " 'okay': 216,\n",
              " 'oke': 217,\n",
              " 'okee': 218,\n",
              " 'okeyv': 219,\n",
              " 'orang': 220,\n",
              " 'ouy': 221,\n",
              " 'pacar': 222,\n",
              " 'pagi': 223,\n",
              " 'paham': 224,\n",
              " 'pakai': 225,\n",
              " 'parah': 226,\n",
              " 'peka': 227,\n",
              " 'pencet': 228,\n",
              " 'permisi': 229,\n",
              " 'pernasalahan': 230,\n",
              " 'pilih': 231,\n",
              " 'produk': 232,\n",
              " 'program': 233,\n",
              " 'pulsa': 234,\n",
              " 'putar': 235,\n",
              " 'ra': 236,\n",
              " 'respon': 237,\n",
              " 'ribet': 238,\n",
              " 'robot': 239,\n",
              " 'rugi': 240,\n",
              " 'rumah': 241,\n",
              " 'saiia': 242,\n",
              " 'salah': 243,\n",
              " 'salam': 244,\n",
              " 'samlekom': 245,\n",
              " 'samlikum': 246,\n",
              " 'sampun': 247,\n",
              " 'sayang': 248,\n",
              " 'sehat': 249,\n",
              " 'sejag': 250,\n",
              " 'sekolah': 251,\n",
              " 'selamat': 252,\n",
              " 'selesai': 253,\n",
              " 'semlekom': 254,\n",
              " 'senang': 255,\n",
              " 'serius': 256,\n",
              " 'sesuai': 257,\n",
              " 'si': 258,\n",
              " 'siaap': 259,\n",
              " 'sibuk': 260,\n",
              " 'sih': 261,\n",
              " 'sij': 262,\n",
              " 'singkirin': 263,\n",
              " 'sinyal': 264,\n",
              " 'sistem': 265,\n",
              " 'sotta': 266,\n",
              " 'speak': 267,\n",
              " 'suka': 268,\n",
              " 'sumpah': 269,\n",
              " 'super': 270,\n",
              " 'syaa': 271,\n",
              " 'taka': 272,\n",
              " 'tanyabisa': 273,\n",
              " 'tanyq': 274,\n",
              " 'tarik': 275,\n",
              " 'tau': 276,\n",
              " 'taut': 277,\n",
              " 'terima': 278,\n",
              " 'terimakasi': 279,\n",
              " 'terimakasih': 280,\n",
              " 'termakasih': 281,\n",
              " 'tertawa': 282,\n",
              " 'tes': 283,\n",
              " 'test': 284,\n",
              " 'testing': 285,\n",
              " 'tetimakasih': 286,\n",
              " 'thank': 287,\n",
              " 'the': 288,\n",
              " 'tidaak': 289,\n",
              " 'tidak': 290,\n",
              " 'tidur': 291,\n",
              " 'tinggal': 292,\n",
              " 'ttp': 293,\n",
              " 'tydak': 294,\n",
              " 'ua': 295,\n",
              " 'uhhh': 296,\n",
              " 'ulang': 297,\n",
              " 'umur': 298,\n",
              " 'unchhhh': 299,\n",
              " 'untung': 300,\n",
              " 'uyyy': 301,\n",
              " 'waah': 302,\n",
              " 'wb': 303,\n",
              " 'wkwkwkwkwkwk': 304,\n",
              " 'wokee': 305,\n",
              " 'woy': 306,\n",
              " 'wr': 307,\n",
              " 'ya': 308,\n",
              " 'yaiyalah': 309,\n",
              " 'yaudahlah': 310,\n",
              " 'yeay': 311,\n",
              " 'yh': 312,\n",
              " 'zzz': 313}"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Melihat Jumlah Fitur\n",
        "print (len(tf_idf.get_feature_names_out()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wAYH0_DwcfAk",
        "outputId": "b19cc713-15a0-4c05-e0a0-4453fceedb97"
      },
      "id": "wAYH0_DwcfAk",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "314\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Melihat vektor kata\n",
        "\n",
        "X_tf_idf = tf_idf.transform(X).toarray()\n",
        "X_tf_idf\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5KN5eRruYod",
        "outputId": "c311219e-13d4-47f3-81a5-bf0cd0248af1"
      },
      "id": "i5KN5eRruYod",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
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              "       ...,\n",
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              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]])"
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          "metadata": {},
          "execution_count": 43
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    {
      "cell_type": "code",
      "source": [
        "data_tf_idf = pd.DataFrame(X_tf_idf,columns=tf_idf.get_feature_names_out())\n",
        "data_tf_idf\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
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        "id": "O0lrfhybuf6P",
        "outputId": "2e42be18-4ab9-47d8-808f-7b27d418d16a"
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      "id": "O0lrfhybuf6P",
      "execution_count": null,
      "outputs": [
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## __3.2 Feature Selection__"
      ],
      "metadata": {
        "id": "w8YPYPb0u6PG"
      },
      "id": "w8YPYPb0u6PG"
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(data_tf_idf) #data_unigram\n",
        "y = np.array(y)\n",
        "\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSfOXvIyuzib",
        "outputId": "63f89249-1fa7-47b6-eadc-eb7e165713d2"
      },
      "id": "LSfOXvIyuzib",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "\n",
        "# Proses Algoritma Chi-Square\n",
        "chi2_features = SelectKBest(chi2, k=152) #k=152\n",
        "X_kbest_features = chi2_features.fit_transform(X, y)\n",
        "\n",
        "# Melihat data\n",
        "print('Original features:', X.shape[1])\n",
        "print('Selected features:', X_kbest_features.shape[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFAwH5gqvBhF",
        "outputId": "00c58df5-e827-474d-ed4f-791436582e6e"
      },
      "id": "mFAwH5gqvBhF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original features: 314\n",
            "Selected features: 152\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat datframe nilai setiap feature dari chi-square\n",
        "\n",
        "data_chi2 = pd.DataFrame(chi2_features.scores_, columns=['Nilai'])\n",
        "data_chi2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pRyWE2ebvEBq",
        "outputId": "fc5c503d-407a-4112-ae03-a88a109a171e"
      },
      "id": "pRyWE2ebvEBq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Nilai\n",
              "0    0.565232\n",
              "1    1.224226\n",
              "2    0.565232\n",
              "3    0.501920\n",
              "4    3.438192\n",
              "..        ...\n",
              "309  5.509804\n",
              "310  0.883245\n",
              "311  4.044155\n",
              "312  3.812514\n",
              "313  2.754902\n",
              "\n",
              "[314 rows x 1 columns]"
            ],
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              "\n",
              "  <div id=\"df-e9d7f909-69f4-4d24-9539-e5b865466087\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nilai</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.565232</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.224226</td>\n",
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              "      <td>0.565232</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.501920</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.438192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>5.509804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>310</th>\n",
              "      <td>0.883245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>4.044155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>3.812514</td>\n",
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              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>2.754902</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>314 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9d7f909-69f4-4d24-9539-e5b865466087')\"\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Menampilkan fitur beserta nilainya\n",
        "feature = tf_idf.get_feature_names_out()\n",
        "data_chi2['Fitur'] = feature\n",
        "data_chi2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RINRWapG0FZD",
        "outputId": "b9100fe9-7298-4b1a-9389-7f058653c06f"
      },
      "id": "RINRWapG0FZD",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Nilai      Fitur\n",
              "0    0.565232        ach\n",
              "1    1.224226       aduh\n",
              "2    0.565232     aduuhh\n",
              "3    0.501920         ae\n",
              "4    3.438192         ah\n",
              "..        ...        ...\n",
              "309  5.509804   yaiyalah\n",
              "310  0.883245  yaudahlah\n",
              "311  4.044155       yeay\n",
              "312  3.812514         yh\n",
              "313  2.754902        zzz\n",
              "\n",
              "[314 rows x 2 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nilai</th>\n",
              "      <th>Fitur</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>1.224226</td>\n",
              "      <td>aduh</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.565232</td>\n",
              "      <td>aduuhh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.501920</td>\n",
              "      <td>ae</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.438192</td>\n",
              "      <td>ah</td>\n",
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              "    <tr>\n",
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              "      <th>309</th>\n",
              "      <td>5.509804</td>\n",
              "      <td>yaiyalah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>310</th>\n",
              "      <td>0.883245</td>\n",
              "      <td>yaudahlah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>4.044155</td>\n",
              "      <td>yeay</td>\n",
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              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>3.812514</td>\n",
              "      <td>yh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>2.754902</td>\n",
              "      <td>zzz</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>314 rows × 2 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          const element = document.querySelector('#df-de6e5ee4-927d-4f9b-9f08-53e3ac89c231');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengurutkan fitur berdasarkan nilai tertinggi\n",
        "data_chi2.sort_values(by='Nilai', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "kso1bnen0S3r",
        "outputId": "08bcba8d-76e9-40b9-f01b-82a0842804de"
      },
      "id": "kso1bnen0S3r",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Nilai        Fitur\n",
              "280  18.533031  terimakasih\n",
              "229  17.157895      permisi\n",
              "143   9.625853        keluh\n",
              "29    8.776141        bagus\n",
              "109   8.264706           hi\n",
              "..         ...          ...\n",
              "126   0.250960           it\n",
              "162   0.250960       losing\n",
              "75    0.250960          cry\n",
              "288   0.250960          the\n",
              "211   0.186124          nya\n",
              "\n",
              "[314 rows x 2 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nilai</th>\n",
              "      <th>Fitur</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>18.533031</td>\n",
              "      <td>terimakasih</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>17.157895</td>\n",
              "      <td>permisi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>9.625853</td>\n",
              "      <td>keluh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8.776141</td>\n",
              "      <td>bagus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>8.264706</td>\n",
              "      <td>hi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>0.250960</td>\n",
              "      <td>it</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>0.250960</td>\n",
              "      <td>losing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>0.250960</td>\n",
              "      <td>cry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>288</th>\n",
              "      <td>0.250960</td>\n",
              "      <td>the</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>0.186124</td>\n",
              "      <td>nya</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>314 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-900b6b70-cafe-460f-ba31-83b651e40453')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-900b6b70-cafe-460f-ba31-83b651e40453 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-900b6b70-cafe-460f-ba31-83b651e40453');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Menampilkan mask pada feature yang terseleksi\n",
        "\n",
        "#False berarti fitur tidak terpilih dan True berarti fitur terpilih\n",
        "mask = chi2_features.get_support()\n",
        "mask\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAQNIpyB0aig",
        "outputId": "4a257b46-4762-4507-bfff-d8c5cba23184"
      },
      "id": "HAQNIpyB0aig",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False,  True, False, False,  True, False,  True,  True, False,\n",
              "       False,  True, False,  True,  True, False, False,  True, False,\n",
              "       False, False, False,  True,  True,  True, False, False, False,\n",
              "       False, False,  True, False,  True,  True,  True,  True, False,\n",
              "        True, False, False, False, False, False, False, False, False,\n",
              "       False, False,  True,  True,  True,  True, False,  True, False,\n",
              "       False,  True,  True, False, False,  True, False, False,  True,\n",
              "       False,  True, False, False,  True, False, False,  True, False,\n",
              "        True, False,  True, False, False, False, False,  True, False,\n",
              "       False, False, False,  True,  True, False,  True,  True,  True,\n",
              "        True,  True, False,  True,  True,  True, False, False,  True,\n",
              "        True,  True, False, False,  True,  True,  True, False, False,\n",
              "       False,  True, False, False, False,  True, False, False, False,\n",
              "        True, False, False,  True, False,  True,  True,  True,  True,\n",
              "       False,  True,  True,  True, False,  True, False,  True, False,\n",
              "        True, False, False, False,  True, False, False, False,  True,\n",
              "       False, False,  True, False, False, False,  True,  True,  True,\n",
              "        True, False,  True,  True,  True,  True, False,  True,  True,\n",
              "       False, False,  True,  True,  True, False, False, False,  True,\n",
              "        True,  True,  True, False, False,  True, False,  True, False,\n",
              "       False,  True,  True, False, False,  True, False,  True,  True,\n",
              "        True,  True, False, False, False, False,  True,  True, False,\n",
              "       False, False, False, False, False, False,  True,  True,  True,\n",
              "       False,  True,  True, False, False, False, False,  True,  True,\n",
              "       False,  True, False, False, False,  True,  True,  True, False,\n",
              "       False, False, False, False,  True, False, False,  True,  True,\n",
              "       False, False, False, False,  True, False,  True,  True,  True,\n",
              "       False,  True,  True, False, False,  True, False,  True, False,\n",
              "        True,  True, False,  True, False,  True, False, False,  True,\n",
              "        True, False, False,  True, False, False,  True,  True, False,\n",
              "        True,  True, False,  True,  True,  True, False, False,  True,\n",
              "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
              "       False, False, False,  True, False, False, False, False, False,\n",
              "       False,  True, False,  True,  True,  True,  True,  True,  True,\n",
              "       False,  True,  True,  True, False,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Menampilkan fitur-fitur terpilih berdasarkan mask atau nilai tertinggi\n",
        "\n",
        "new_features = []\n",
        "\n",
        "for bool, f in zip(mask,feature):\n",
        "  if bool:\n",
        "    new_features.append(f)\n",
        "  selected_features = new_features\n",
        "selected_features"
      ],
      "metadata": {
        "id": "s-Ui9uLT0fXw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "975b15a8-29ca-4e02-9ea9-42ad9e21969a"
      },
      "id": "s-Ui9uLT0fXw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['aduh',\n",
              " 'ah',\n",
              " 'alaah',\n",
              " 'alhamdullilah',\n",
              " 'aman',\n",
              " 'anjeng',\n",
              " 'anjir',\n",
              " 'apa',\n",
              " 'assalamualaiku',\n",
              " 'assalamualaikumin',\n",
              " 'asslamualaikum',\n",
              " 'bagus',\n",
              " 'bak',\n",
              " 'balas',\n",
              " 'bang',\n",
              " 'banget',\n",
              " 'barusan',\n",
              " 'bilang',\n",
              " 'bingung',\n",
              " 'blue',\n",
              " 'bngt',\n",
              " 'bobo',\n",
              " 'bom',\n",
              " 'book',\n",
              " 'bosen',\n",
              " 'butuh',\n",
              " 'cai',\n",
              " 'cek',\n",
              " 'cinta',\n",
              " 'complient',\n",
              " 'coyyy',\n",
              " 'deh',\n",
              " 'diam',\n",
              " 'donk',\n",
              " 'dude',\n",
              " 'dukung',\n",
              " 'eh',\n",
              " 'english',\n",
              " 'erti',\n",
              " 'fa',\n",
              " 'full',\n",
              " 'gai',\n",
              " 'go',\n",
              " 'guna',\n",
              " 'hadeeeeuh',\n",
              " 'halo',\n",
              " 'halon',\n",
              " 'har',\n",
              " 'hi',\n",
              " 'hobby',\n",
              " 'hu',\n",
              " 'ihhh',\n",
              " 'informasi',\n",
              " 'internet',\n",
              " 'isi',\n",
              " 'istri',\n",
              " 'jam',\n",
              " 'jaring',\n",
              " 'jawabanya',\n",
              " 'jjkakak',\n",
              " 'jumpa',\n",
              " 'kabar',\n",
              " 'kasih',\n",
              " 'keluh',\n",
              " 'klik',\n",
              " 'komplain',\n",
              " 'komputer',\n",
              " 'ku',\n",
              " 'kuya',\n",
              " 'kwkwkwkw',\n",
              " 'kwlwkwkwkkwkwkw',\n",
              " 'lahir',\n",
              " 'layan',\n",
              " 'logout',\n",
              " 'loh',\n",
              " 'love',\n",
              " 'lumayan',\n",
              " 'maaf',\n",
              " 'makasiih',\n",
              " 'makasiiihh',\n",
              " 'maksud',\n",
              " 'malam',\n",
              " 'mana',\n",
              " 'masmba',\n",
              " 'maya',\n",
              " 'mbak',\n",
              " 'menyamoaikan',\n",
              " 'mi',\n",
              " 'milih',\n",
              " 'min',\n",
              " 'misi',\n",
              " 'muncul',\n",
              " 'nama',\n",
              " 'nih',\n",
              " 'niih',\n",
              " 'njit',\n",
              " 'nnya',\n",
              " 'nomor',\n",
              " 'oh',\n",
              " 'ok',\n",
              " 'oke',\n",
              " 'ouy',\n",
              " 'pacar',\n",
              " 'pagi',\n",
              " 'permisi',\n",
              " 'produk',\n",
              " 'program',\n",
              " 'ribet',\n",
              " 'rugi',\n",
              " 'rumah',\n",
              " 'saiia',\n",
              " 'salam',\n",
              " 'samlekom',\n",
              " 'sayang',\n",
              " 'sejag',\n",
              " 'selamat',\n",
              " 'selesai',\n",
              " 'senang',\n",
              " 'sesuai',\n",
              " 'sibuk',\n",
              " 'sih',\n",
              " 'sinyal',\n",
              " 'speak',\n",
              " 'suka',\n",
              " 'super',\n",
              " 'syaa',\n",
              " 'tanyabisa',\n",
              " 'tanyq',\n",
              " 'tarik',\n",
              " 'terima',\n",
              " 'terimakasi',\n",
              " 'terimakasih',\n",
              " 'termakasih',\n",
              " 'tertawa',\n",
              " 'tes',\n",
              " 'test',\n",
              " 'tetimakasih',\n",
              " 'thank',\n",
              " 'tidur',\n",
              " 'umur',\n",
              " 'untung',\n",
              " 'uyyy',\n",
              " 'waah',\n",
              " 'wb',\n",
              " 'wkwkwkwkwkwk',\n",
              " 'wokee',\n",
              " 'wr',\n",
              " 'ya',\n",
              " 'yaiyalah',\n",
              " 'yeay',\n",
              " 'yh',\n",
              " 'zzz']"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat vocabulary baru berdasarkan fitur yang terseleksi\n",
        "#Ini digunakan untuk mengenerate fitur vector tf-idf\n",
        "\n",
        "new_selected_features = {}\n",
        "\n",
        "for (k,v) in tf_idf.vocabulary_.items():\n",
        "    if k in selected_features:\n",
        "        new_selected_features[k] = v\n",
        "\n",
        "new_selected_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MosNatVBbMAc",
        "outputId": "b0ca4cba-47d7-4d9b-e503-6900e1350033"
      },
      "id": "MosNatVBbMAc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'aduh': 1,\n",
              " 'ah': 4,\n",
              " 'alaah': 6,\n",
              " 'alhamdullilah': 7,\n",
              " 'aman': 10,\n",
              " 'anjeng': 12,\n",
              " 'anjir': 13,\n",
              " 'apa': 16,\n",
              " 'assalamualaiku': 21,\n",
              " 'assalamualaikumin': 22,\n",
              " 'asslamualaikum': 23,\n",
              " 'bagus': 29,\n",
              " 'bak': 31,\n",
              " 'balas': 32,\n",
              " 'bang': 33,\n",
              " 'banget': 34,\n",
              " 'barusan': 36,\n",
              " 'bilang': 47,\n",
              " 'bingung': 48,\n",
              " 'blue': 49,\n",
              " 'bngt': 50,\n",
              " 'bobo': 52,\n",
              " 'bom': 55,\n",
              " 'book': 56,\n",
              " 'bosen': 59,\n",
              " 'butuh': 62,\n",
              " 'cai': 64,\n",
              " 'cek': 67,\n",
              " 'cinta': 70,\n",
              " 'complient': 72,\n",
              " 'coyyy': 74,\n",
              " 'deh': 79,\n",
              " 'diam': 84,\n",
              " 'donk': 85,\n",
              " 'dude': 87,\n",
              " 'dukung': 88,\n",
              " 'eh': 89,\n",
              " 'english': 90,\n",
              " 'erti': 91,\n",
              " 'fa': 93,\n",
              " 'full': 94,\n",
              " 'gai': 95,\n",
              " 'go': 98,\n",
              " 'guna': 99,\n",
              " 'hadeeeeuh': 100,\n",
              " 'halo': 103,\n",
              " 'halon': 104,\n",
              " 'har': 105,\n",
              " 'hi': 109,\n",
              " 'hobby': 113,\n",
              " 'hu': 117,\n",
              " 'ihhh': 120,\n",
              " 'informasi': 122,\n",
              " 'internet': 123,\n",
              " 'isi': 124,\n",
              " 'istri': 125,\n",
              " 'jam': 127,\n",
              " 'jaring': 128,\n",
              " 'jawabanya': 129,\n",
              " 'jjkakak': 131,\n",
              " 'jumpa': 133,\n",
              " 'kabar': 135,\n",
              " 'kasih': 139,\n",
              " 'keluh': 143,\n",
              " 'klik': 146,\n",
              " 'komplain': 150,\n",
              " 'komputer': 151,\n",
              " 'ku': 152,\n",
              " 'kuya': 153,\n",
              " 'kwkwkwkw': 155,\n",
              " 'kwlwkwkwkkwkwkw': 156,\n",
              " 'lahir': 157,\n",
              " 'layan': 158,\n",
              " 'logout': 160,\n",
              " 'loh': 161,\n",
              " 'love': 164,\n",
              " 'lumayan': 165,\n",
              " 'maaf': 166,\n",
              " 'makasiih': 170,\n",
              " 'makasiiihh': 171,\n",
              " 'maksud': 172,\n",
              " 'malam': 173,\n",
              " 'mana': 176,\n",
              " 'masmba': 178,\n",
              " 'maya': 181,\n",
              " 'mbak': 182,\n",
              " 'menyamoaikan': 185,\n",
              " 'mi': 187,\n",
              " 'milih': 188,\n",
              " 'min': 189,\n",
              " 'misi': 190,\n",
              " 'muncul': 195,\n",
              " 'nama': 196,\n",
              " 'nih': 204,\n",
              " 'niih': 205,\n",
              " 'njit': 206,\n",
              " 'nnya': 208,\n",
              " 'nomor': 209,\n",
              " 'oh': 214,\n",
              " 'ok': 215,\n",
              " 'oke': 217,\n",
              " 'ouy': 221,\n",
              " 'pacar': 222,\n",
              " 'pagi': 223,\n",
              " 'permisi': 229,\n",
              " 'produk': 232,\n",
              " 'program': 233,\n",
              " 'ribet': 238,\n",
              " 'rugi': 240,\n",
              " 'rumah': 241,\n",
              " 'saiia': 242,\n",
              " 'salam': 244,\n",
              " 'samlekom': 245,\n",
              " 'sayang': 248,\n",
              " 'sejag': 250,\n",
              " 'selamat': 252,\n",
              " 'selesai': 253,\n",
              " 'senang': 255,\n",
              " 'sesuai': 257,\n",
              " 'sibuk': 260,\n",
              " 'sih': 261,\n",
              " 'sinyal': 264,\n",
              " 'speak': 267,\n",
              " 'suka': 268,\n",
              " 'super': 270,\n",
              " 'syaa': 271,\n",
              " 'tanyabisa': 273,\n",
              " 'tanyq': 274,\n",
              " 'tarik': 275,\n",
              " 'terima': 278,\n",
              " 'terimakasi': 279,\n",
              " 'terimakasih': 280,\n",
              " 'termakasih': 281,\n",
              " 'tertawa': 282,\n",
              " 'tes': 283,\n",
              " 'test': 284,\n",
              " 'tetimakasih': 286,\n",
              " 'thank': 287,\n",
              " 'tidur': 291,\n",
              " 'umur': 298,\n",
              " 'untung': 300,\n",
              " 'uyyy': 301,\n",
              " 'waah': 302,\n",
              " 'wb': 303,\n",
              " 'wkwkwkwkwkwk': 304,\n",
              " 'wokee': 305,\n",
              " 'wr': 307,\n",
              " 'ya': 308,\n",
              " 'yaiyalah': 309,\n",
              " 'yeay': 311,\n",
              " 'yh': 312,\n",
              " 'zzz': 313}"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Melihat jumlah fitur baru \n",
        "#Ini sesuai dengan nilai k pada chi-square\n",
        "len(new_selected_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HNMai2q0dFsg",
        "outputId": "e47589bb-dad4-4b51-9218-56622168f04e"
      },
      "id": "HNMai2q0dFsg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "152"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Memilih fitur terbaik dan memasukannya kedalam Dataframe\n",
        "data_selected_features = pd.DataFrame(X_kbest_features,columns=selected_features)\n",
        "data_selected_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "zmm_cxcE0hf1",
        "outputId": "c60280ea-110a-4477-8951-c6bc49c0710a"
      },
      "id": "zmm_cxcE0hf1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __4. Modelling (Machine Learning)__"
      ],
      "metadata": {
        "id": "B2lyRjUvXirN"
      },
      "id": "B2lyRjUvXirN"
    },
    {
      "cell_type": "code",
      "source": [
        "#Import Library\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#Algoritme\n",
        "\n",
        "#Algoritme Support Vector Machine\n",
        "from sklearn import svm\n",
        "#Naive Bayes\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "#Algoritma Random Forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "#Algoritma Decision Tree\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "1oeAs-adXq1p"
      },
      "id": "1oeAs-adXq1p",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data_selected_features\n",
        "y = df_klasifikasi['label']\n",
        "#Memisihkan data training dan data testing dengan perbandingan 75:25\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.25, random_state=6)"
      ],
      "metadata": {
        "id": "VfXoXmRFXzDI"
      },
      "id": "VfXoXmRFXzDI",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print\n",
        "print('Banyak data x_train :',len(x_train))\n",
        "print('Banyak data x_test  :',len(x_test))\n",
        "print('Banyak data y_train :',len(y_train))\n",
        "print('Banyak data y_test  :',len(y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2sxf0BrdYud",
        "outputId": "6e1a31a4-98ec-40b7-c8ea-ed8d65a0d872"
      },
      "id": "T2sxf0BrdYud",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Banyak data x_train : 287\n",
            "Banyak data x_test  : 96\n",
            "Banyak data y_train : 287\n",
            "Banyak data y_test  : 96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Training Model\n",
        "\n",
        "from datetime import datetime\n",
        "start_time = datetime.now()\n",
        "\n",
        "#algoritme fitting\n",
        "\n",
        "#text_algorithm = MultinomialNB()\n",
        "text_algorithm = svm.SVC(kernel=\"rbf\", C=1.0)\n",
        "#text_algorithm = RandomForestClassifier()\n",
        "#text_algorithm = DecisionTreeClassifier()\n",
        "model = text_algorithm.fit(x_train, y_train)\n",
        "\n",
        "\n",
        "end_time = datetime.now()\n",
        "result_time  =end_time-start_time\n",
        "print(\"Duration:\",result_time)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kkwg9JrmevdF",
        "outputId": "d27730c6-c634-45f8-8d3d-79e4c8a7907b"
      },
      "id": "Kkwg9JrmevdF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duration: 0:00:00.020116\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediksi\n",
        "predicted = model.predict(x_test)\n",
        "predicted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGXZlnobe52C",
        "outputId": "73e063a6-6d87-4a3f-922c-12a4b338e78e"
      },
      "id": "EGXZlnobe52C",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['negative', 'negative', 'negative', 'negative', 'negative',\n",
              "       'negative', 'positive', 'negative', 'positive', 'negative',\n",
              "       'negative', 'negative', 'negative', 'positive', 'positive',\n",
              "       'negative', 'negative', 'negative', 'negative', 'negative',\n",
              "       'negative', 'negative', 'positive', 'negative', 'negative',\n",
              "       'positive', 'positive', 'positive', 'negative', 'negative',\n",
              "       'positive', 'positive', 'negative', 'positive', 'negative',\n",
              "       'positive', 'negative', 'positive', 'negative', 'negative',\n",
              "       'positive', 'negative', 'negative', 'positive', 'positive',\n",
              "       'positive', 'negative', 'negative', 'positive', 'negative',\n",
              "       'positive', 'negative', 'negative', 'negative', 'negative',\n",
              "       'positive', 'negative', 'negative', 'positive', 'negative',\n",
              "       'positive', 'positive', 'positive', 'negative', 'negative',\n",
              "       'positive', 'negative', 'negative', 'negative', 'positive',\n",
              "       'positive', 'negative', 'negative', 'negative', 'positive',\n",
              "       'negative', 'negative', 'negative', 'positive', 'positive',\n",
              "       'negative', 'negative', 'neutral', 'negative', 'negative',\n",
              "       'neutral', 'negative', 'positive', 'negative', 'negative',\n",
              "       'negative', 'positive', 'negative', 'negative', 'negative',\n",
              "       'positive'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __5. Model Evaluation__"
      ],
      "metadata": {
        "id": "Ks4_w_gffa_s"
      },
      "id": "Ks4_w_gffa_s"
    },
    {
      "cell_type": "code",
      "source": [
        "#Library evaluasi\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "#Mengkomputasi performa model\n",
        "NewprediksiBenar = (predicted == y_test).sum()\n",
        "NewprediksiSalah = (predicted != y_test).sum()\n",
        "    \n",
        "print(\"prediksi benar: \", NewprediksiBenar, \" data\")\n",
        "print(\"prediksi salah: \", NewprediksiSalah, \" data\")\n",
        "print(\"Akurasi Algoritma: \", NewprediksiBenar/(NewprediksiBenar+NewprediksiSalah)*100,\"%\")\n",
        "\n",
        "# Menggunakan Confusion Matrix untuk Model Evaluasi\n",
        "CM = confusion_matrix(y_test,predicted)\n",
        "\n",
        "TN = CM[0][0]\n",
        "FN = CM[1][0]\n",
        "TP = CM[1][1]\n",
        "FP = CM[0][1]\n",
        "precision    = TP/(TP+FP)\n",
        "recall       = TP/(TP+FN)\n",
        "f1 = 2*recall*precision/(recall+precision)\n",
        "\n",
        "print (\"TRUE NEGATIVE (TN):\",TN)\n",
        "print (\"FALSE NEGATIVE (FN):\",FN)\n",
        "print (\"TRUE POSITIVE (TP):\",TP)\n",
        "print (\"FALSE POSITIVE (FP):\",FP)\n",
        "print (\"PRECISION:\",precision*100,\"%\")\n",
        "print (\"RECALL:\",recall*100,\"%\")\n",
        "print (\"F1:\",f1*100,\"%\")\n",
        "\n",
        "print(classification_report(y_test,predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecw3OWKDfDtm",
        "outputId": "15c74b5d-62fd-4888-e0f2-25684c99e98a"
      },
      "id": "ecw3OWKDfDtm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prediksi benar:  75  data\n",
            "prediksi salah:  21  data\n",
            "Akurasi Algoritma:  78.125 %\n",
            "TRUE NEGATIVE (TN): 52\n",
            "FALSE NEGATIVE (FN): 5\n",
            "TRUE POSITIVE (TP): 2\n",
            "FALSE POSITIVE (FP): 0\n",
            "PRECISION: 100.0 %\n",
            "RECALL: 28.57142857142857 %\n",
            "F1: 44.44444444444445 %\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.85      0.96      0.90        54\n",
            "     neutral       1.00      0.12      0.21        17\n",
            "    positive       0.64      0.84      0.72        25\n",
            "\n",
            "    accuracy                           0.78        96\n",
            "   macro avg       0.83      0.64      0.61        96\n",
            "weighted avg       0.82      0.78      0.73        96\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Menggunakan Cross Validation untuk memvalidasi data\n",
        "\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cv        = ShuffleSplit(n_splits=10, test_size=0.25, random_state=6)\n",
        "accuracy  = (cross_val_score(model,x,y,cv=cv,scoring='accuracy'))\n",
        "score_accuracy = np.mean(accuracy)\n",
        "print (\"accuracy: \",accuracy)\n",
        "print (\"accuracy: \",score_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8jQ3iKlvfW0I",
        "outputId": "1b1db909-ade3-40f5-ce90-538b789d0a01"
      },
      "id": "8jQ3iKlvfW0I",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy:  [0.78125    0.76041667 0.82291667 0.75       0.80208333 0.79166667\n",
            " 0.75       0.76041667 0.76041667 0.80208333]\n",
            "accuracy:  0.778125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Kesimpulan\n",
        "\n",
        "Setelah dilakukannya pemodelan dengan berbagai macam model klasifikasi seperti Random Forest, Decision Tree, Naive Bayes, Support Vector Machine, dll. \n",
        "\n",
        "Didapatkan hasil algoritma model yang paling tinggi akurasinya yaitu menggunakan SVM (Support Vector Machine) dengan akurasi tertinggi 78.1% dengan evaluasi model confusion matrix untuk Precission = 100%, Recall = 28.57, F1 = 44.44%. Serta untuk Cross Validation mendapatkan score 77%.\n"
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