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      "metadata": {
        "id": "f4a1399a-f23d-4060-a07e-bce5a5c7ddac"
      },
      "source": [
        "# Klasifikasi Teks menggunakan ANN\n",
        "## Fahrizal Setiawan\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "53a214ae-c9cf-4d46-925d-068f1685537b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53a214ae-c9cf-4d46-925d-068f1685537b",
        "outputId": "f224e8ff-e3a6-49d9-fac9-cafc0202eb4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Classification Report ===\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.33      1.00      0.50         1\n",
            "    positive       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           0.33         3\n",
            "   macro avg       0.17      0.50      0.25         3\n",
            "weighted avg       0.11      0.33      0.17         3\n",
            "\n",
            "=== Confusion Matrix ===\n",
            "[[1 0]\n",
            " [2 0]]\n",
            "\n",
            "Prediksi untuk: barang buruk, saya kecewa\n",
            "Hasil: negative\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ],
      "source": [
        "# ---------------------------------------------------------\n",
        "# Klasifikasi Teks dengan TF-IDF + Feedforward Neural Network\n",
        "# ---------------------------------------------------------\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# -----------------------------------------\n",
        "# 1. Contoh Dataset\n",
        "# -----------------------------------------\n",
        "# Anda bisa mengganti dataset ini dengan dataset lain (CSV, JSON, dll)\n",
        "\n",
        "data = {\n",
        "    \"text\": [\n",
        "        \"Saya suka produk ini, luar biasa\",\n",
        "        \"Layanannya buruk, saya sangat kecewa\",\n",
        "        \"Penjual tidak responsif, sangat kecewa\",\n",
        "        \"Pembelian terbaik yang pernah saya lakukan\",\n",
        "        \"Saya benci produk ini, buang-buang uang\",\n",
        "        \"Kualitasnya sangat bagus, direkomendasikan\",\n",
        "        \"Pengalaman buruk, tidak akan membeli lagi\",\n",
        "\n",
        "    ],\n",
        "    \"label\": [\n",
        "        \"positive\",\n",
        "        \"negative\",\n",
        "        \"negative\", # Corrected: Was positive, now negative to match sentiment\n",
        "        \"positive\", # Corrected: Was negative, now positive to match sentiment\n",
        "        \"negative\", # Corrected: Was positive, now negative to match sentiment\n",
        "        \"positive\", # Corrected: Was negative, now positive to match sentiment\n",
        "        \"negative\",\n",
        "        # Added missing label to match length of 'text' list\n",
        "    ]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# -----------------------------------------\n",
        "# 2. Split Train & Test\n",
        "# -----------------------------------------\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df[\"text\"], df[\"label\"], test_size=0.3, random_state=42\n",
        ")\n",
        "\n",
        "# -----------------------------------------\n",
        "# 3. TF-IDF Vectorization\n",
        "# -----------------------------------------\n",
        "tfidf = TfidfVectorizer(max_features=5000)\n",
        "X_train_tfidf = tfidf.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf.transform(X_test)\n",
        "\n",
        "# -----------------------------------------\n",
        "# 4. Feedforward ANN (MLPClassifier)\n",
        "# -----------------------------------------\n",
        "model = MLPClassifier(\n",
        "    hidden_layer_sizes=(256, 64),\n",
        "    activation='relu',\n",
        "    solver='adam',\n",
        "    max_iter=500,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "model.fit(X_train_tfidf, y_train)\n",
        "\n",
        "# -----------------------------------------\n",
        "# 5. Evaluasi Model\n",
        "# -----------------------------------------\n",
        "y_pred = model.predict(X_test_tfidf)\n",
        "\n",
        "print(\"=== Classification Report ===\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"=== Confusion Matrix ===\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "# -----------------------------------------\n",
        "# 6. Prediksi Teks Baru\n",
        "# -----------------------------------------\n",
        "#sample_text = [\"barang bagus luar biasa\"]\n",
        "sample_text = [\"barang buruk, saya kecewa\"]\n",
        "sample_vec = tfidf.transform(sample_text)\n",
        "prediction = model.predict(sample_vec)\n",
        "\n",
        "print(\"\\nPrediksi untuk:\", sample_text[0])\n",
        "print(\"Hasil:\", prediction[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "9f7d90fe-4af4-446c-9547-c9312bfa6fc7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f7d90fe-4af4-446c-9547-c9312bfa6fc7",
        "outputId": "4a889f91-ff57-459e-8987-43a230489899"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Prediksi untuk: saya benci barang ini\n",
            "Hasil: negative\n"
          ]
        }
      ],
      "source": [
        "#sample_text = [\"barang bagus luar biasa\"]\n",
        "sample_text = [\"saya benci barang ini\"]\n",
        "sample_vec = tfidf.transform(sample_text)\n",
        "prediction = model.predict(sample_vec)\n",
        "print(\"\\nPrediksi untuk:\", sample_text[0])\n",
        "print(\"Hasil:\", prediction[0])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "metadata": {
        "id": "d4b9a7c2-0f08-43fd-8da8-018d839a4917"
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      "source": []
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