{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "EMnX7B87MgpN",
        "outputId": "14ed4621-d746-48a9-a97a-f9c96958d1f2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9ea630b8-f055-4575-8ebd-97b8f1c3abbb\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-9ea630b8-f055-4575-8ebd-97b8f1c3abbb\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving ulasan_balanced.csv to ulasan_balanced.csv\n"
          ]
        }
      ],
      "source": [
        "# 📌 STEP 1: Upload File\n",
        "from google.colab import files  # Gunakan ini kalau di Google Colab\n",
        "uploaded = files.upload()\n",
        "\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0TZG8_QDMhUM",
        "outputId": "b52f732e-7f0a-4816-e8ea-a66a52def34f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jumlah total data: 801\n"
          ]
        }
      ],
      "source": [
        "# Baca file yang diupload\n",
        "df = pd.read_csv(list(uploaded.keys())[0])\n",
        "print(\"Jumlah total data:\", df.shape[0])\n",
        "df = df[['content', 'sentimen']]  # pastikan kolom ini tersedia"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 941,
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        },
        "id": "VK1jOCnsMkmu",
        "outputId": "d9190c46-c62b-4788-d623-0d450b41c1ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.52.4)\n",
            "Collecting nlpaug\n",
            "  Downloading nlpaug-1.1.11-py3-none-any.whl.metadata (14 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.18.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.30.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.32.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2.0.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2024.11.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.21.1)\n",
            "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.5.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.11/dist-packages (from transformers) (4.67.1)\n",
            "Requirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from nlpaug) (2.2.2)\n",
            "Requirement already satisfied: gdown>=4.0.0 in /usr/local/lib/python3.11/dist-packages (from nlpaug) (5.2.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.11/dist-packages (from gdown>=4.0.0->nlpaug) (4.13.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (2025.3.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (4.14.0)\n",
            "Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (1.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2.0->nlpaug) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2.0->nlpaug) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2.0->nlpaug) (2025.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.4.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2.4.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2025.4.26)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=1.2.0->nlpaug) (1.17.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4->gdown>=4.0.0->nlpaug) (2.7)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.11/dist-packages (from requests[socks]->gdown>=4.0.0->nlpaug) (1.7.1)\n",
            "Downloading nlpaug-1.1.11-py3-none-any.whl (410 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.5/410.5 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: nlpaug\n",
            "Successfully installed nlpaug-1.1.11\n",
            "Train: 640, Test: 120, Val: 41\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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              "model_id": "60a4813f62f4466ba2a71b7c3362fc68",
              "version_major": 2,
              "version_minor": 0
            },
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              "tokenizer_config.json:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
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          "data": {
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              "vocab.txt:   0%|          | 0.00/229k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            ]
          },
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        },
        {
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            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/498M [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following layers were not sharded: encoder.layer.*.output.LayerNorm.bias, encoder.layer.*.attention.output.dense.bias, encoder.layer.*.output.dense.bias, encoder.layer.*.attention.self.key.weight, encoder.layer.*.attention.self.query.weight, embeddings.word_embeddings.weight, encoder.layer.*.intermediate.dense.weight, embeddings.token_type_embeddings.weight, encoder.layer.*.attention.self.key.bias, encoder.layer.*.intermediate.dense.bias, embeddings.position_embeddings.weight, encoder.layer.*.attention.self.value.weight, encoder.layer.*.output.dense.weight, encoder.layer.*.attention.output.LayerNorm.weight, embeddings.LayerNorm.bias, encoder.layer.*.attention.output.LayerNorm.bias, encoder.layer.*.output.LayerNorm.weight, encoder.layer.*.attention.self.query.bias, encoder.layer.*.attention.self.value.bias, embeddings.LayerNorm.weight, encoder.layer.*.attention.output.dense.weight\n"
          ]
        },
        {
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              "model_id": "58444e78b25840978b90b7ac64e58d00",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/498M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Install dependencies jika belum\n",
        "!pip install transformers nlpaug\n",
        "\n",
        "# Import\n",
        "import nlpaug.augmenter.word as naw\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split dataset\n",
        "df_temp, df_val = train_test_split(df, test_size=0.05, random_state=42, stratify=df['sentimen'])\n",
        "test_size_adjusted = 0.15 / (1 - 0.05)\n",
        "df_train, df_test = train_test_split(df_temp, test_size=test_size_adjusted, random_state=42, stratify=df_temp['sentimen'])\n",
        "\n",
        "print(f\"Train: {df_train.shape[0]}, Test: {df_test.shape[0]}, Val: {df_val.shape[0]}\")\n",
        "\n",
        "# Load IndoBERT augmenter\n",
        "aug = naw.ContextualWordEmbsAug(\n",
        "    model_path='indobenchmark/indobert-base-p1',\n",
        "    action=\"insert\",\n",
        "    top_k=10,\n",
        "    device='cpu'\n",
        ")\n",
        "\n",
        "# Fungsi augmentasi lebih banyak\n",
        "def augment_text(text, n=5):  # <- di sini kamu bisa set mau augment berapa kali\n",
        "    try:\n",
        "        augmented = aug.augment(text, n=n)\n",
        "        return augmented if isinstance(augmented, list) else [augmented]\n",
        "    except Exception as e:\n",
        "        print(f\"Error augmenting: {e}\")\n",
        "        return []\n",
        "\n",
        "# Lakukan augmentasi\n",
        "augmented_rows = []\n",
        "for idx, row in df_train.iterrows():\n",
        "    aug_texts = augment_text(row['content'], n=5)  # <-- augment 5x per data\n",
        "    for aug_text in aug_texts:\n",
        "        augmented_rows.append({'content': aug_text, 'sentimen': row['sentimen']})\n",
        "    if idx % 500 == 0:\n",
        "        print(f\"Augmenting row {idx}/{df_train.shape[0]}\")\n",
        "\n",
        "# Gabungkan hasil augmentasi\n",
        "df_augmented = pd.DataFrame(augmented_rows)\n",
        "df_train_augmented = pd.concat([df_train, df_augmented], ignore_index=True)\n",
        "\n",
        "print(f\"Jumlah data setelah augmentasi: {df_train_augmented.shape[0]}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "036vmokdOYnd"
      },
      "outputs": [],
      "source": [
        "pip install Sastrawi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yYi1S6Y1OZcd"
      },
      "outputs": [],
      "source": [
        "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
        "factory = StemmerFactory()\n",
        "stemmer = factory.create_stemmer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zV0pVMvNMo0I"
      },
      "outputs": [],
      "source": [
        "# 📌 STEP 3: Preprocessing Teks (SETELAH AUGMENTASI)\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "stop_words = set(stopwords.words('indonesian'))\n",
        "\n",
        "def clean_text(text):\n",
        "    text = str(text).lower()\n",
        "    text = re.sub(r'http\\S+|www\\S+', '', text)          # hapus URL\n",
        "    text = re.sub(r'[^a-zA-Z\\s]', '', text)             # hapus simbol, angka, dll\n",
        "    text = re.sub(r'[\\U00010000-\\U0010ffff]', '', text) # hapus emoji\n",
        "    words = text.split()\n",
        "    words = [w for w in words if w not in stop_words and len(w) > 2]\n",
        "    return words\n",
        "\n",
        "# Terapkan ke semua bagian\n",
        "df_train_augmented['clean'] = df_train_augmented['content'].apply(clean_text)\n",
        "df_test['clean'] = df_test['content'].apply(clean_text)\n",
        "df_val['clean'] = df_val['content'].apply(clean_text)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "62OU3kQ0MttG"
      },
      "outputs": [],
      "source": [
        "# Gabungkan token hasil cleaning menjadi string\n",
        "df_train_augmented['clean_str'] = df_train_augmented['clean'].apply(lambda x: ' '.join(x))\n",
        "df_test['clean_str'] = df_test['clean'].apply(lambda x: ' '.join(x))\n",
        "df_val['clean_str'] = df_val['clean'].apply(lambda x: ' '.join(x))\n",
        "\n",
        "# TF-IDF Vectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "vectorizer = TfidfVectorizer()\n",
        "X_train = vectorizer.fit_transform(df_train_augmented['clean_str'])\n",
        "X_test = vectorizer.transform(df_test['clean_str'])\n",
        "X_val = vectorizer.transform(df_val['clean_str'])\n",
        "\n",
        "# Target labels\n",
        "y_train = df_train_augmented['sentimen']\n",
        "y_test = df_test['sentimen']\n",
        "y_val = df_val['sentimen']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U5f-DKCSMvHt"
      },
      "outputs": [],
      "source": [
        "# 📌 STEP 5: Latih Naive Bayes\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "model = MultinomialNB()\n",
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4QWA5b0XMwNF"
      },
      "outputs": [],
      "source": [
        "# 📌 STEP 6: Evaluasi\n",
        "print(\"\\n📊 Evaluasi pada Test Set:\")\n",
        "y_pred_test = model.predict(X_test)\n",
        "print(\"Akurasi:\", accuracy_score(y_test, y_pred_test))\n",
        "print(classification_report(y_test, y_pred_test))\n",
        "\n",
        "print(\"\\n📊 Evaluasi pada Validation Set:\")\n",
        "y_pred_val = model.predict(X_val)\n",
        "print(\"Akurasi:\", accuracy_score(y_val, y_pred_val))\n",
        "print(classification_report(y_val, y_pred_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ax79G3jticm8"
      },
      "outputs": [],
      "source": [
        "import joblib\n",
        "joblib.dump(model, 'model_nb.pkl')\n",
        "joblib.dump(vectorizer, 'tfidf_vectorizer.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JMNNKXcXM2kJ"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "# CONFUSION MATRIX - Test\n",
        "cm_test = confusion_matrix(y_test, y_pred_test, labels=model.classes_)\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "sns.heatmap(cm_test, annot=True, fmt='d', cmap='Blues', xticklabels=model.classes_, yticklabels=model.classes_)\n",
        "plt.title('Confusion Matrix - Test Set')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()\n",
        "\n",
        "# CONFUSION MATRIX - Validation\n",
        "cm_val = confusion_matrix(y_val, y_pred_val, labels=model.classes_)\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "sns.heatmap(cm_val, annot=True, fmt='d', cmap='Greens', xticklabels=model.classes_, yticklabels=model.classes_)\n",
        "plt.title('Confusion Matrix - Validation Set')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()\n",
        "\n",
        "# DISTRIBUSI PREDIKSI - Test\n",
        "plt.figure(figsize=(6, 4))\n",
        "sns.countplot(x=y_pred_test, order=model.classes_, palette='viridis')\n",
        "plt.title('Distribusi Prediksi - Test Set')\n",
        "plt.xlabel('Label Prediksi')\n",
        "plt.ylabel('Jumlah')\n",
        "plt.show()\n",
        "\n",
        "# WORD CLOUD PER SENTIMEN (opsional & keren)\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sentimen_list = ['positif', 'negatif', 'netral']\n",
        "for label in sentimen_list:\n",
        "    # Gabungkan token jadi string per baris → lalu semua baris jadi satu string\n",
        "    text = \" \".join(df_train_augmented[df_train_augmented['sentimen'] == label]['clean'].apply(lambda x: ' '.join(x)))\n",
        "\n",
        "    wc = WordCloud(width=800, height=400, background_color='white').generate(text)\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    plt.imshow(wc, interpolation='bilinear')\n",
        "    plt.axis('off')\n",
        "    plt.title(f'Word Cloud - {label.upper()}')\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZJj_FGsiNLka"
      },
      "outputs": [],
      "source": [
        "# Plot distribusi sentimen dalam train set\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(6, 4))\n",
        "sns.countplot(x=df_train['sentimen'], order=df_train_augmented['sentimen'].value_counts().index, palette='Set2')\n",
        "plt.title(\"Distribusi Sentimen di Train Set\")\n",
        "plt.xlabel(\"Sentimen\")\n",
        "plt.ylabel(\"Jumlah Data\")\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQHF89mmhH2L"
      },
      "source": [
        "## save file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IEzeVAuJhP2s"
      },
      "outputs": [],
      "source": [
        "df_train.to_csv(\"train_original.csv\", index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iZ1a01flNofP"
      },
      "outputs": [],
      "source": [
        "# Simpan ke CSV versi augmented (3x lipat)\n",
        "df_train_augmented.to_csv(\"train_augmented.csv\", index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HS0v7k4whJNt"
      },
      "outputs": [],
      "source": [
        "# Simpan test dan val\n",
        "df_test.to_csv(\"test.csv\", index=False)\n",
        "df_val.to_csv(\"val.csv\", index=False)"
      ]
    }
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