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     "text": [
      "2024-07-10 00:56:24.521445: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-07-10 00:56:24.521553: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-07-10 00:56:24.669517: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "# Import All Library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Matploitlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sklearndf = pd.read_csv(\"/kaggle/input/benign-malicious-phpopcode/opcode_php.csv\")\n",
    "from sklearn.model_selection import train_test_split, cross_validate\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "# tensorflow\n",
    "from tensorflow.keras.preprocessing.text import one_hot,Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Conv1D, MaxPooling1D, GlobalMaxPooling1D, Flatten, Dense, Dropout\n",
    "from tensorflow.keras.initializers import Constant\n",
    "from tensorflow.keras.preprocessing.text import one_hot,Tokenizer\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "# Gensim\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "# Seaborn\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f655978",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Get Dataset\n",
    "df = pd.read_csv(\"/kaggle/input/benign-malicious-phpopcode/opcode_php.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae531901",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T00:56:51.752931Z",
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   "outputs": [],
   "source": [
    "# Opcode\n",
    "opcode = []\n",
    "for op in df['Result']:\n",
    "    opcode.append(op.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df84c2c1",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11750597, 46402920)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Word2Vec Embedding\n",
    "dimension = 100 \n",
    "w2v_model=Word2Vec(sentences=opcode,vector_size=dimension,window=10,min_count=5, sg=0)\n",
    "w2v_model.train(opcode,epochs=10,total_examples=len(opcode))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1abe81ce",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# Tokenizing & embadding matric\n",
    "vocab=w2v_model.wv.key_to_index\n",
    "vocab = list(vocab.keys())\n",
    "\n",
    "word_vec_dict={}\n",
    "for word in vocab:\n",
    "    word_vec_dict[word]=w2v_model.wv.get_vector(word)\n",
    "\n",
    "max_rev_len = 200 \n",
    "token = Tokenizer(lower=False, filters=\"\")\n",
    "token.fit_on_texts(df['Result'])\n",
    "vocab_size = len(token.word_index) + 1\n",
    "encd_rev = token.texts_to_sequences(df['Result'])\n",
    "pad_rev = pad_sequences(encd_rev, maxlen=max_rev_len, padding='post')\n",
    "\n",
    "embed_matrix = np.zeros(shape=(vocab_size, dimension))\n",
    "for word, i in token.word_index.items():\n",
    "    embed_vector = word_vec_dict.get(word)\n",
    "    if embed_vector is not None:\n",
    "        embed_matrix[i] = embed_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e17e6b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T00:57:25.204440Z",
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   },
   "outputs": [],
   "source": [
    "# Training Split\n",
    "Y=to_categorical(df['Status']) \n",
    "x_train,x_test,y_train,y_test=train_test_split(pad_rev,Y,test_size=0.20,random_state=42)"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m46/69\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7829 - loss: 0.4935"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1720573054.649268     131 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 91ms/step - accuracy: 0.8259 - loss: 0.4074 - val_accuracy: 0.9655 - val_loss: 0.1036\n",
      "Epoch 2/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.0859 - val_accuracy: 0.9764 - val_loss: 0.0816\n",
      "Epoch 3/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9854 - loss: 0.0517 - val_accuracy: 0.9809 - val_loss: 0.0668\n",
      "Epoch 4/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9874 - loss: 0.0405 - val_accuracy: 0.9818 - val_loss: 0.0654\n",
      "Epoch 5/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9928 - loss: 0.0261 - val_accuracy: 0.9818 - val_loss: 0.0630\n",
      "Epoch 6/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9906 - loss: 0.0300 - val_accuracy: 0.9818 - val_loss: 0.0683\n",
      "Epoch 7/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9923 - loss: 0.0241 - val_accuracy: 0.9827 - val_loss: 0.0712\n",
      "Epoch 8/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9931 - loss: 0.0207 - val_accuracy: 0.9837 - val_loss: 0.0676\n",
      "Epoch 9/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9916 - loss: 0.0238 - val_accuracy: 0.9827 - val_loss: 0.0693\n",
      "Epoch 10/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9921 - loss: 0.0227 - val_accuracy: 0.9827 - val_loss: 0.0748\n",
      "Epoch 11/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9936 - loss: 0.0204 - val_accuracy: 0.9837 - val_loss: 0.0774\n",
      "Epoch 12/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9936 - loss: 0.0207 - val_accuracy: 0.9827 - val_loss: 0.0796\n",
      "Epoch 13/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9934 - loss: 0.0196 - val_accuracy: 0.9827 - val_loss: 0.0773\n",
      "Epoch 14/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9961 - loss: 0.0155 - val_accuracy: 0.9837 - val_loss: 0.0811\n",
      "Epoch 15/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9931 - loss: 0.0186 - val_accuracy: 0.9837 - val_loss: 0.0807\n",
      "Epoch 16/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9940 - loss: 0.0195 - val_accuracy: 0.9827 - val_loss: 0.0878\n",
      "Epoch 17/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9956 - loss: 0.0157 - val_accuracy: 0.9837 - val_loss: 0.0846\n",
      "Epoch 18/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9961 - loss: 0.0144 - val_accuracy: 0.9837 - val_loss: 0.0869\n",
      "Epoch 19/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9943 - loss: 0.0177 - val_accuracy: 0.9837 - val_loss: 0.0869\n",
      "Epoch 20/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9934 - loss: 0.0190 - val_accuracy: 0.9818 - val_loss: 0.0936\n"
     ]
    }
   ],
   "source": [
    "# CNN Model\n",
    "# Inisialisasi\n",
    "cnn_model = Sequential([\n",
    "    Embedding(input_dim=vocab_size, \n",
    "              output_dim=dimension, \n",
    "              embeddings_initializer=Constant(embed_matrix)\n",
    "             ),    \n",
    "    Conv1D(128, 5, activation='relu'),\n",
    "    GlobalMaxPooling1D(),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dropout(0.2),\n",
    "    Dense(2, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compile\n",
    "cnn_model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy', \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Training\n",
    "# Epoch dan Batch size awal\n",
    "history = cnn_model.fit(x_train, y_train, \n",
    "                        epochs=20, \n",
    "                        batch_size=64, \n",
    "                        validation_data=(x_test, y_test))"
   ]
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisasi training\n",
    "def plot_training_history(history):\n",
    "    # Accuracy\n",
    "    plt.figure(figsize=(14, 5))\n",
    "    \n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "    plt.title('Accuracy over Epochs')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "\n",
    "    # Loss\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['loss'], label='Train Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "    plt.title('Loss over Epochs')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "plot_training_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e2b1502",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T00:57:46.657002Z",
     "iopub.status.busy": "2024-07-10T00:57:46.656683Z",
     "iopub.status.idle": "2024-07-10T00:57:46.679967Z",
     "shell.execute_reply": "2024-07-10T00:57:46.679108Z"
    },
    "papermill": {
     "duration": 0.046023,
     "end_time": "2024-07-10T00:57:46.681999",
     "exception": false,
     "start_time": "2024-07-10T00:57:46.635976",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,300</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">64,128</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_max_pooling1d            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling1D</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">258</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m, \u001b[38;5;34m100\u001b[0m)       │         \u001b[38;5;34m9,300\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1d (\u001b[38;5;33mConv1D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │        \u001b[38;5;34m64,128\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_max_pooling1d            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m16,512\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │           \u001b[38;5;34m258\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">270,596</span> (1.03 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m270,596\u001b[0m (1.03 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">90,198</span> (352.34 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m90,198\u001b[0m (352.34 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">180,398</span> (704.68 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m180,398\u001b[0m (704.68 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Model Summary\n",
    "cnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77ae78fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T00:57:46.725603Z",
     "iopub.status.busy": "2024-07-10T00:57:46.725337Z",
     "iopub.status.idle": "2024-07-10T00:57:48.994962Z",
     "shell.execute_reply": "2024-07-10T00:57:48.993386Z"
    },
    "papermill": {
     "duration": 2.296483,
     "end_time": "2024-07-10T00:57:49.000051",
     "exception": false,
     "start_time": "2024-07-10T00:57:46.703568",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CNN Model Evaluation:\n",
      "Loss: 0.0936\n",
      "Accuracy: 0.9818\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
      "\n",
      "Classification Report for CNN Model:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98       518\n",
      "           1       0.99      0.98      0.98       583\n",
      "\n",
      "    accuracy                           0.98      1101\n",
      "   macro avg       0.98      0.98      0.98      1101\n",
      "weighted avg       0.98      0.98      0.98      1101\n",
      "\n",
      "\n",
      "Confusion Matrix for CNN Model:\n",
      "[[512   6]\n",
      " [ 14 569]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "cnn_scores = cnn_model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"\\nCNN Model Evaluation:\")\n",
    "print(\"Loss: {:.4f}\".format(cnn_scores[0]))\n",
    "print(\"Accuracy: {:.4f}\".format(cnn_scores[1]))\n",
    "\n",
    "# Predictions for CNN model\n",
    "cnn_y_pred = cnn_model.predict(x_test)\n",
    "cnn_y_pred_classes = np.argmax(cnn_y_pred, axis=1)\n",
    "\n",
    "# Classification report and confusion matrix for CNN model\n",
    "print(\"\\nClassification Report for CNN Model:\")\n",
    "print(classification_report(np.argmax(y_test, axis=1), cnn_y_pred_classes))\n",
    "print(\"\\nConfusion Matrix for CNN Model:\")\n",
    "conf_matrix = confusion_matrix(np.argmax(y_test, axis=1), cnn_y_pred_classes)\n",
    "print(conf_matrix)\n",
    "\n",
    "# Visualize the confusion matrix\n",
    "def plot_confusion_matrix(conf_matrix):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.xlabel('Predicted Label')\n",
    "    plt.ylabel('True Label')\n",
    "    plt.show()\n",
    "\n",
    "plot_confusion_matrix(conf_matrix)"
   ]
  },
  {
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    {
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     "text": [
      "Epoch 1/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.8413 - loss: 1.0491 - val_accuracy: 0.9619 - val_loss: 0.4841\n",
      "Epoch 2/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9593 - loss: 0.4208 - val_accuracy: 0.9664 - val_loss: 0.2614\n",
      "Epoch 3/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9699 - loss: 0.2180 - val_accuracy: 0.9691 - val_loss: 0.1760\n",
      "Epoch 4/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9694 - loss: 0.1608 - val_accuracy: 0.9691 - val_loss: 0.1384\n",
      "Epoch 5/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9802 - loss: 0.1086 - val_accuracy: 0.9755 - val_loss: 0.1149\n",
      "Epoch 6/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9829 - loss: 0.0841 - val_accuracy: 0.9718 - val_loss: 0.1200\n",
      "Epoch 7/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9776 - loss: 0.0922 - val_accuracy: 0.9709 - val_loss: 0.1181\n",
      "Epoch 8/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9849 - loss: 0.0689 - val_accuracy: 0.9728 - val_loss: 0.1113\n",
      "Epoch 9/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9847 - loss: 0.0683 - val_accuracy: 0.9755 - val_loss: 0.1164\n",
      "Epoch 10/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9838 - loss: 0.0620 - val_accuracy: 0.9728 - val_loss: 0.1091\n",
      "Epoch 11/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9797 - loss: 0.0768 - val_accuracy: 0.9691 - val_loss: 0.1142\n",
      "Epoch 12/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9873 - loss: 0.0626 - val_accuracy: 0.9709 - val_loss: 0.1177\n",
      "Epoch 13/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9850 - loss: 0.0552 - val_accuracy: 0.9773 - val_loss: 0.1021\n",
      "Epoch 14/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9906 - loss: 0.0454 - val_accuracy: 0.9791 - val_loss: 0.0912\n",
      "Epoch 15/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9911 - loss: 0.0424 - val_accuracy: 0.9782 - val_loss: 0.0980\n",
      "Epoch 16/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9903 - loss: 0.0399 - val_accuracy: 0.9764 - val_loss: 0.1144\n",
      "Epoch 17/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9909 - loss: 0.0381 - val_accuracy: 0.9791 - val_loss: 0.1061\n",
      "Epoch 18/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.9923 - loss: 0.0325 - val_accuracy: 0.9746 - val_loss: 0.1072\n",
      "Epoch 19/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.9921 - loss: 0.0353 - val_accuracy: 0.9782 - val_loss: 0.1016\n",
      "Epoch 20/20\n",
      "\u001b[1m69/69\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9920 - loss: 0.0328 - val_accuracy: 0.9709 - val_loss: 0.1336\n"
     ]
    }
   ],
   "source": [
    "#RNN + LSTM\n",
    "# Inisialisasi\n",
    "rnn_model = Sequential([\n",
    "    Embedding(input_dim=vocab_size, \n",
    "              output_dim=dimension, \n",
    "              embeddings_initializer=Constant(embed_matrix),\n",
    "              mask_zero=True),\n",
    "    LSTM(128, \n",
    "         activation='tanh', \n",
    "         recurrent_activation='sigmoid', \n",
    "         recurrent_dropout=0, \n",
    "         unroll=False, \n",
    "         use_bias=True),\n",
    "    Dropout(0.2), \n",
    "    Dense(64, activation='relu', kernel_regularizer='l2'),\n",
    "    Dense(Y.shape[1], activation='softmax')\n",
    "])\n",
    "\n",
    "# Compile\n",
    "rnn_model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Training\n",
    "rnn_history = rnn_model.fit(x_train, y_train, \n",
    "                        epochs=20, \n",
    "                        batch_size=64, \n",
    "                        validation_data=(x_test, y_test))"
   ]
  },
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,300</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">117,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">130</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m, \u001b[38;5;34m100\u001b[0m)       │         \u001b[38;5;34m9,300\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m117,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │           \u001b[38;5;34m130\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
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     "metadata": {},
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">404,804</span> (1.54 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m404,804\u001b[0m (1.54 MB)\n"
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     "metadata": {},
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">134,934</span> (527.09 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m134,934\u001b[0m (527.09 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">269,870</span> (1.03 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m269,870\u001b[0m (1.03 MB)\n"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "# Model Summary\n",
    "rnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4bd3278f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T00:58:12.100920Z",
     "iopub.status.busy": "2024-07-10T00:58:12.100641Z",
     "iopub.status.idle": "2024-07-10T00:58:12.579010Z",
     "shell.execute_reply": "2024-07-10T00:58:12.578054Z"
    },
    "papermill": {
     "duration": 0.528936,
     "end_time": "2024-07-10T00:58:12.581006",
     "exception": false,
     "start_time": "2024-07-10T00:58:12.052070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisasi training\n",
    "plot_training_history(rnn_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc2e262e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T00:58:12.681257Z",
     "iopub.status.busy": "2024-07-10T00:58:12.680933Z",
     "iopub.status.idle": "2024-07-10T00:58:13.568408Z",
     "shell.execute_reply": "2024-07-10T00:58:13.567461Z"
    },
    "papermill": {
     "duration": 0.939933,
     "end_time": "2024-07-10T00:58:13.570425",
     "exception": false,
     "start_time": "2024-07-10T00:58:12.630492",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RNN Model Evaluation:\n",
      "Loss: 0.1336\n",
      "Accuracy: 0.9709\n",
      "\u001b[1m35/35\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\n",
      "Classification Report for RNN Model:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.96      0.97       518\n",
      "           1       0.97      0.98      0.97       583\n",
      "\n",
      "    accuracy                           0.97      1101\n",
      "   macro avg       0.97      0.97      0.97      1101\n",
      "weighted avg       0.97      0.97      0.97      1101\n",
      "\n",
      "\n",
      "Confusion Matrix for RNN Model:\n",
      "[[499  19]\n",
      " [ 13 570]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "rnn_scores = rnn_model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"\\nRNN Model Evaluation:\")\n",
    "print(\"Loss: {:.4f}\".format(rnn_scores[0]))\n",
    "print(\"Accuracy: {:.4f}\".format(rnn_scores[1]))\n",
    "\n",
    "# Predictions for CNN model\n",
    "rnn_y_pred = rnn_model.predict(x_test)\n",
    "rnn_y_pred_classes = np.argmax(rnn_y_pred, axis=1)\n",
    "\n",
    "# Classification report and confusion matrix for RNN model\n",
    "print(\"\\nClassification Report for RNN Model:\")\n",
    "print(classification_report(np.argmax(y_test, axis=1), rnn_y_pred_classes))\n",
    "print(\"\\nConfusion Matrix for RNN Model:\")\n",
    "conf_matrix = confusion_matrix(np.argmax(y_test, axis=1), rnn_y_pred_classes)\n",
    "print(conf_matrix)\n",
    "\n",
    "# Visualize the confusion matrix\n",
    "def plot_confusion_matrix(conf_matrix):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.xlabel('Predicted Label')\n",
    "    plt.ylabel('True Label')\n",
    "    plt.show()\n",
    "\n",
    "plot_confusion_matrix(conf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a612113",
   "metadata": {
    "papermill": {
     "duration": 0.050543,
     "end_time": "2024-07-10T00:58:13.670960",
     "exception": false,
     "start_time": "2024-07-10T00:58:13.620417",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Evaluasi, perbandingan perbedaan epoch, bath_size, max_rev_len untuk melihat akurasi tertinggi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "717edab2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T00:58:13.774431Z",
     "iopub.status.busy": "2024-07-10T00:58:13.773536Z",
     "iopub.status.idle": "2024-07-10T00:58:13.781821Z",
     "shell.execute_reply": "2024-07-10T00:58:13.780703Z"
    },
    "papermill": {
     "duration": 0.062445,
     "end_time": "2024-07-10T00:58:13.783726",
     "exception": false,
     "start_time": "2024-07-10T00:58:13.721281",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### CNN Evaluasi\n",
    "def cnn_train_model(epochs, batch_size, max_rev_len):\n",
    "    model = Sequential([\n",
    "        Embedding(input_dim=vocab_size, output_dim=dimension, input_length=max_rev_len, embeddings_initializer=Constant(embed_matrix)),    \n",
    "        Conv1D(128, 5, activation='relu'),\n",
    "        GlobalMaxPooling1D(),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dropout(0.2),\n",
    "        Dense(2, activation='sigmoid')\n",
    "    ])\n",
    "    \n",
    "    model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy', \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "    history = model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(x_test, y_test), verbose=0)\n",
    "    scores = model.evaluate(x_test, y_test, verbose=0)\n",
    "    return scores[1], history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1e07d820",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T00:58:13.884508Z",
     "iopub.status.busy": "2024-07-10T00:58:13.883914Z",
     "iopub.status.idle": "2024-07-10T01:09:22.725339Z",
     "shell.execute_reply": "2024-07-10T01:09:22.724427Z"
    },
    "papermill": {
     "duration": 668.894584,
     "end_time": "2024-07-10T01:09:22.728010",
     "exception": false,
     "start_time": "2024-07-10T00:58:13.833426",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
      "  warnings.warn(\n",
      "2024-07-10 00:58:36.777484: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng12{k11=2} for conv (f32[128,128,1,196]{3,2,1,0}, u8[0]{0}) custom-call(f32[128,100,1,200]{3,2,1,0}, f32[128,100,1,5]{3,2,1,0}, f32[128]{0}), window={size=1x5}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBiasActivationForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kRelu\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n",
      "2024-07-10 00:58:38.067189: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 2.28981216s\n",
      "Trying algorithm eng12{k11=2} for conv (f32[128,128,1,196]{3,2,1,0}, u8[0]{0}) custom-call(f32[128,100,1,200]{3,2,1,0}, f32[128,100,1,5]{3,2,1,0}, f32[128]{0}), window={size=1x5}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBiasActivationForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kRelu\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n",
      "2024-07-10 00:58:39.174592: E external/local_xla/xla/service/slow_operation_alarm.cc:65] Trying algorithm eng12{k11=2} for conv (f32[128,128,1,196]{3,2,1,0}, u8[0]{0}) custom-call(f32[128,100,1,200]{3,2,1,0}, f32[128,100,1,5]{3,2,1,0}, f32[128]{0}), window={size=1x5}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBiasActivationForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kRelu\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n",
      "2024-07-10 00:58:40.501597: E external/local_xla/xla/service/slow_operation_alarm.cc:133] The operation took 2.327184864s\n",
      "Trying algorithm eng12{k11=2} for conv (f32[128,128,1,196]{3,2,1,0}, u8[0]{0}) custom-call(f32[128,100,1,200]{3,2,1,0}, f32[128,100,1,5]{3,2,1,0}, f32[128]{0}), window={size=1x5}, dim_labels=bf01_oi01->bf01, custom_call_target=\"__cudnn$convBiasActivationForward\", backend_config={\"conv_result_scale\":1,\"activation_mode\":\"kRelu\",\"side_input_scale\":0,\"leakyrelu_alpha\":0} is taking a while...\n"
     ]
    }
   ],
   "source": [
    "results_cnn = []\n",
    "for epochs in range(10, 110, 10):\n",
    "    for batch_size in [32, 64, 128]:\n",
    "        accuracy, _ = cnn_train_model(epochs, batch_size, max_rev_len)\n",
    "        results_cnn.append((epochs, batch_size, accuracy))\n",
    "\n",
    "results_df_cnn = pd.DataFrame(results_cnn, columns=['Epochs', 'Batch Size', 'Accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a5d5120",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:09:22.832476Z",
     "iopub.status.busy": "2024-07-10T01:09:22.832129Z",
     "iopub.status.idle": "2024-07-10T01:09:22.849158Z",
     "shell.execute_reply": "2024-07-10T01:09:22.848303Z"
    },
    "papermill": {
     "duration": 0.07141,
     "end_time": "2024-07-10T01:09:22.851272",
     "exception": false,
     "start_time": "2024-07-10T01:09:22.779862",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epochs</th>\n",
       "      <th>Batch Size</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>0.983651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>128</td>\n",
       "      <td>0.984559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "      <td>0.983651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>128</td>\n",
       "      <td>0.981835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>0.978202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30</td>\n",
       "      <td>128</td>\n",
       "      <td>0.980018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>40</td>\n",
       "      <td>64</td>\n",
       "      <td>0.983651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>40</td>\n",
       "      <td>128</td>\n",
       "      <td>0.980926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>0.981835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>50</td>\n",
       "      <td>128</td>\n",
       "      <td>0.981835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>0.983651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>128</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>70</td>\n",
       "      <td>32</td>\n",
       "      <td>0.983651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>70</td>\n",
       "      <td>64</td>\n",
       "      <td>0.983651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>70</td>\n",
       "      <td>128</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>0.980926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>80</td>\n",
       "      <td>64</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>80</td>\n",
       "      <td>128</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>90</td>\n",
       "      <td>32</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>90</td>\n",
       "      <td>64</td>\n",
       "      <td>0.984559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>90</td>\n",
       "      <td>128</td>\n",
       "      <td>0.983651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>100</td>\n",
       "      <td>32</td>\n",
       "      <td>0.985468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>0.983651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>100</td>\n",
       "      <td>128</td>\n",
       "      <td>0.980926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Epochs  Batch Size  Accuracy\n",
       "0       10          32  0.982743\n",
       "1       10          64  0.983651\n",
       "2       10         128  0.984559\n",
       "3       20          32  0.982743\n",
       "4       20          64  0.983651\n",
       "5       20         128  0.981835\n",
       "6       30          32  0.978202\n",
       "7       30          64  0.982743\n",
       "8       30         128  0.980018\n",
       "9       40          32  0.982743\n",
       "10      40          64  0.983651\n",
       "11      40         128  0.980926\n",
       "12      50          32  0.981835\n",
       "13      50          64  0.982743\n",
       "14      50         128  0.981835\n",
       "15      60          32  0.982743\n",
       "16      60          64  0.983651\n",
       "17      60         128  0.982743\n",
       "18      70          32  0.983651\n",
       "19      70          64  0.983651\n",
       "20      70         128  0.982743\n",
       "21      80          32  0.980926\n",
       "22      80          64  0.982743\n",
       "23      80         128  0.982743\n",
       "24      90          32  0.982743\n",
       "25      90          64  0.984559\n",
       "26      90         128  0.983651\n",
       "27     100          32  0.985468\n",
       "28     100          64  0.983651\n",
       "29     100         128  0.980926"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df_cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "245c8a7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:09:22.954165Z",
     "iopub.status.busy": "2024-07-10T01:09:22.953842Z",
     "iopub.status.idle": "2024-07-10T01:09:23.298824Z",
     "shell.execute_reply": "2024-07-10T01:09:23.297859Z"
    },
    "papermill": {
     "duration": 0.398858,
     "end_time": "2024-07-10T01:09:23.301242",
     "exception": false,
     "start_time": "2024-07-10T01:09:22.902384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results\n",
    "fig_cnn, ax_cnn = plt.subplots(figsize=(10, 6))\n",
    "for batch_size in [32, 64, 128]:\n",
    "    batch_results = results_df_cnn[results_df_cnn['Batch Size'] == batch_size]\n",
    "    ax_cnn.plot(batch_results['Epochs'], batch_results['Accuracy'], marker='o', label=f'Batch Size {batch_size}')\n",
    "ax_cnn.set_title('Accuracy for Different Epochs and Batch Sizes')\n",
    "ax_cnn.set_xlabel('Epochs')\n",
    "ax_cnn.set_ylabel('Accuracy')\n",
    "ax_cnn.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae462bd6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:09:23.406624Z",
     "iopub.status.busy": "2024-07-10T01:09:23.406277Z",
     "iopub.status.idle": "2024-07-10T01:09:23.411952Z",
     "shell.execute_reply": "2024-07-10T01:09:23.411013Z"
    },
    "papermill": {
     "duration": 0.060741,
     "end_time": "2024-07-10T01:09:23.414073",
     "exception": false,
     "start_time": "2024-07-10T01:09:23.353332",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Ambil best result CNN\n",
    "best_result_cnn = results_df_cnn.loc[results_df_cnn['Accuracy'].idxmax()]\n",
    "best_epochs_cnn = int(best_result_cnn['Epochs'])\n",
    "best_batch_size_cnn = int(best_result_cnn['Batch Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7121c6b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:09:23.522307Z",
     "iopub.status.busy": "2024-07-10T01:09:23.522010Z",
     "iopub.status.idle": "2024-07-10T01:09:23.526701Z",
     "shell.execute_reply": "2024-07-10T01:09:23.525838Z"
    },
    "papermill": {
     "duration": 0.059337,
     "end_time": "2024-07-10T01:09:23.528950",
     "exception": false,
     "start_time": "2024-07-10T01:09:23.469613",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "32\n"
     ]
    }
   ],
   "source": [
    "print(best_epochs_cnn)\n",
    "print(best_batch_size_cnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "12a619b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:09:23.631741Z",
     "iopub.status.busy": "2024-07-10T01:09:23.631439Z",
     "iopub.status.idle": "2024-07-10T01:09:23.635107Z",
     "shell.execute_reply": "2024-07-10T01:09:23.634264Z"
    },
    "papermill": {
     "duration": 0.056906,
     "end_time": "2024-07-10T01:09:23.636895",
     "exception": false,
     "start_time": "2024-07-10T01:09:23.579989",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lanjut \n",
    "# max_rev_len_results_cnn = []\n",
    "# for max_len in range(100, 1100, 100):\n",
    "#     accuracy, history = cnn_train_model(best_epochs_cnn, best_batch_size_cnn, max_len)\n",
    "#     max_rev_len_results_cnn.append((max_len, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "250fb21e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:09:23.742218Z",
     "iopub.status.busy": "2024-07-10T01:09:23.741914Z",
     "iopub.status.idle": "2024-07-10T01:09:23.745909Z",
     "shell.execute_reply": "2024-07-10T01:09:23.745019Z"
    },
    "papermill": {
     "duration": 0.05913,
     "end_time": "2024-07-10T01:09:23.747969",
     "exception": false,
     "start_time": "2024-07-10T01:09:23.688839",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert max_rev_len results to DataFrame\n",
    "# max_rev_len_df_cnn = pd.DataFrame(max_rev_len_results_cnn, columns=['Max Rev Len', 'Accuracy'])\n",
    "\n",
    "# max_rev_len_df_cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0de2339f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:09:23.852409Z",
     "iopub.status.busy": "2024-07-10T01:09:23.851662Z",
     "iopub.status.idle": "2024-07-10T01:09:23.855557Z",
     "shell.execute_reply": "2024-07-10T01:09:23.854732Z"
    },
    "papermill": {
     "duration": 0.057936,
     "end_time": "2024-07-10T01:09:23.857533",
     "exception": false,
     "start_time": "2024-07-10T01:09:23.799597",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Plot max_rev_len results\n",
    "# plt.figure(figsize=(10, 6))\n",
    "# plt.plot(max_rev_len_df_cnn['Max Rev Len'], max_rev_len_df_cnn['Accuracy'], marker='o')\n",
    "# plt.title('Accuracy for Different Max Rev Len CNN')\n",
    "# plt.xlabel('Max Rev Len')\n",
    "# plt.ylabel('Accuracy')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb081d3",
   "metadata": {
    "papermill": {
     "duration": 0.051685,
     "end_time": "2024-07-10T01:09:23.961398",
     "exception": false,
     "start_time": "2024-07-10T01:09:23.909713",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "RNN Evaluasi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e6ebbda5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:09:24.067012Z",
     "iopub.status.busy": "2024-07-10T01:09:24.066581Z",
     "iopub.status.idle": "2024-07-10T01:09:24.074664Z",
     "shell.execute_reply": "2024-07-10T01:09:24.073860Z"
    },
    "papermill": {
     "duration": 0.062472,
     "end_time": "2024-07-10T01:09:24.076543",
     "exception": false,
     "start_time": "2024-07-10T01:09:24.014071",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### RNN Evaluasi\n",
    "def rnn_train_model(epochs, batch_size, max_rev_len):\n",
    "    model = Sequential([\n",
    "        Embedding(input_dim=vocab_size, \n",
    "              output_dim=dimension, \n",
    "              embeddings_initializer=Constant(embed_matrix),\n",
    "              mask_zero=True),\n",
    "        LSTM(128, \n",
    "             activation='tanh', \n",
    "             recurrent_activation='sigmoid', \n",
    "             recurrent_dropout=0, \n",
    "             unroll=False, \n",
    "             use_bias=True),\n",
    "        Dropout(0.2), \n",
    "        Dense(64, activation='relu', kernel_regularizer='l2'),\n",
    "        Dense(Y.shape[1], activation='softmax')\n",
    "    ])\n",
    "    \n",
    "    model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "    history = model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(x_test, y_test), verbose=0)\n",
    "    scores = model.evaluate(x_test, y_test, verbose=0)\n",
    "    return scores[1], history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2dec34af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:09:24.180286Z",
     "iopub.status.busy": "2024-07-10T01:09:24.179992Z",
     "iopub.status.idle": "2024-07-10T01:38:31.737579Z",
     "shell.execute_reply": "2024-07-10T01:38:31.736380Z"
    },
    "papermill": {
     "duration": 1747.67447,
     "end_time": "2024-07-10T01:38:31.802305",
     "exception": false,
     "start_time": "2024-07-10T01:09:24.127835",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Epochs</th>\n",
       "      <th>Batch Size</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>0.956403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>0.973660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>128</td>\n",
       "      <td>0.976385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>0.976385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "      <td>0.978202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>128</td>\n",
       "      <td>0.975477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>0.980926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>0.974569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30</td>\n",
       "      <td>128</td>\n",
       "      <td>0.979110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>0.980018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>40</td>\n",
       "      <td>64</td>\n",
       "      <td>0.979110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>40</td>\n",
       "      <td>128</td>\n",
       "      <td>0.975477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>0.980926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>0.981835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>50</td>\n",
       "      <td>128</td>\n",
       "      <td>0.980018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>0.977293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>0.978202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>128</td>\n",
       "      <td>0.975477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>70</td>\n",
       "      <td>32</td>\n",
       "      <td>0.982743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>70</td>\n",
       "      <td>64</td>\n",
       "      <td>0.980926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>70</td>\n",
       "      <td>128</td>\n",
       "      <td>0.979110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>0.981835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>80</td>\n",
       "      <td>64</td>\n",
       "      <td>0.962761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>80</td>\n",
       "      <td>128</td>\n",
       "      <td>0.974569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>90</td>\n",
       "      <td>32</td>\n",
       "      <td>0.981835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>90</td>\n",
       "      <td>64</td>\n",
       "      <td>0.969119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>90</td>\n",
       "      <td>128</td>\n",
       "      <td>0.977293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>100</td>\n",
       "      <td>32</td>\n",
       "      <td>0.980926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>0.980926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>100</td>\n",
       "      <td>128</td>\n",
       "      <td>0.979110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Epochs  Batch Size  Accuracy\n",
       "0       10          32  0.956403\n",
       "1       10          64  0.973660\n",
       "2       10         128  0.976385\n",
       "3       20          32  0.976385\n",
       "4       20          64  0.978202\n",
       "5       20         128  0.975477\n",
       "6       30          32  0.980926\n",
       "7       30          64  0.974569\n",
       "8       30         128  0.979110\n",
       "9       40          32  0.980018\n",
       "10      40          64  0.979110\n",
       "11      40         128  0.975477\n",
       "12      50          32  0.980926\n",
       "13      50          64  0.981835\n",
       "14      50         128  0.980018\n",
       "15      60          32  0.977293\n",
       "16      60          64  0.978202\n",
       "17      60         128  0.975477\n",
       "18      70          32  0.982743\n",
       "19      70          64  0.980926\n",
       "20      70         128  0.979110\n",
       "21      80          32  0.981835\n",
       "22      80          64  0.962761\n",
       "23      80         128  0.974569\n",
       "24      90          32  0.981835\n",
       "25      90          64  0.969119\n",
       "26      90         128  0.977293\n",
       "27     100          32  0.980926\n",
       "28     100          64  0.980926\n",
       "29     100         128  0.979110"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_rnn = []\n",
    "for epochs in range(10, 110, 10):\n",
    "    for batch_size in [32, 64, 128]:\n",
    "        accuracy, _ = rnn_train_model(epochs, batch_size, max_rev_len)\n",
    "        results_rnn.append((epochs, batch_size, accuracy))\n",
    "\n",
    "results_df_rnn = pd.DataFrame(results_rnn, columns=['Epochs', 'Batch Size', 'Accuracy'])\n",
    "results_df_rnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "308c27d8",
   "metadata": {
    "execution": {
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     "duration": 0.378534,
     "end_time": "2024-07-10T01:38:32.233182",
     "exception": false,
     "start_time": "2024-07-10T01:38:31.854648",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results\n",
    "fig_rnn, ax_rnn = plt.subplots(figsize=(10, 6))\n",
    "for batch_size in [32, 64, 128]:\n",
    "    batch_results = results_df_rnn[results_df_rnn['Batch Size'] == batch_size]\n",
    "    ax_rnn.plot(batch_results['Epochs'], batch_results['Accuracy'], marker='o', label=f'Batch Size {batch_size}')\n",
    "ax_rnn.set_title('Accuracy for Different Epochs and Batch Sizes')\n",
    "ax_rnn.set_xlabel('Epochs')\n",
    "ax_rnn.set_ylabel('Accuracy')\n",
    "ax_rnn.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "87090638",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:38:32.342112Z",
     "iopub.status.busy": "2024-07-10T01:38:32.341562Z",
     "iopub.status.idle": "2024-07-10T01:38:32.346581Z",
     "shell.execute_reply": "2024-07-10T01:38:32.345762Z"
    },
    "papermill": {
     "duration": 0.061703,
     "end_time": "2024-07-10T01:38:32.348677",
     "exception": false,
     "start_time": "2024-07-10T01:38:32.286974",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Ambil best result RNN\n",
    "best_result_rnn = results_df_rnn.loc[results_df_rnn['Accuracy'].idxmax()]\n",
    "best_epochs_rnn = int(best_result_rnn['Epochs'])\n",
    "best_batch_size_rnn = int(best_result_rnn['Batch Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "87a6fba7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:38:32.457763Z",
     "iopub.status.busy": "2024-07-10T01:38:32.457461Z",
     "iopub.status.idle": "2024-07-10T01:38:32.462069Z",
     "shell.execute_reply": "2024-07-10T01:38:32.461206Z"
    },
    "papermill": {
     "duration": 0.060411,
     "end_time": "2024-07-10T01:38:32.463881",
     "exception": false,
     "start_time": "2024-07-10T01:38:32.403470",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "32\n"
     ]
    }
   ],
   "source": [
    "print(best_epochs_cnn)\n",
    "print(best_batch_size_cnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8b80078e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:38:32.571669Z",
     "iopub.status.busy": "2024-07-10T01:38:32.571425Z",
     "iopub.status.idle": "2024-07-10T01:38:32.575941Z",
     "shell.execute_reply": "2024-07-10T01:38:32.575257Z"
    },
    "papermill": {
     "duration": 0.060494,
     "end_time": "2024-07-10T01:38:32.577741",
     "exception": false,
     "start_time": "2024-07-10T01:38:32.517247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lanjut \n",
    "# max_rev_len_results_rnn = []\n",
    "# for max_len in range(100, 1100, 100):\n",
    "#     accuracy, history = rnn_train_model(best_epochs_rnn, best_batch_size_rnn, max_len)\n",
    "#     max_rev_len_results_rnn.append((max_len, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "78432983",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:38:32.685457Z",
     "iopub.status.busy": "2024-07-10T01:38:32.685192Z",
     "iopub.status.idle": "2024-07-10T01:38:32.689081Z",
     "shell.execute_reply": "2024-07-10T01:38:32.688267Z"
    },
    "papermill": {
     "duration": 0.060255,
     "end_time": "2024-07-10T01:38:32.691128",
     "exception": false,
     "start_time": "2024-07-10T01:38:32.630873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# max_rev_len_df_rnn = pd.DataFrame(max_rev_len_results_rnn, columns=['Max Rev Len', 'Accuracy'])\n",
    "# max_rev_len_df_rnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a3cc0b6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:38:32.802808Z",
     "iopub.status.busy": "2024-07-10T01:38:32.802018Z",
     "iopub.status.idle": "2024-07-10T01:38:32.806052Z",
     "shell.execute_reply": "2024-07-10T01:38:32.805220Z"
    },
    "papermill": {
     "duration": 0.06252,
     "end_time": "2024-07-10T01:38:32.808243",
     "exception": false,
     "start_time": "2024-07-10T01:38:32.745723",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(10, 6))\n",
    "# plt.plot(max_rev_len_df_rnn['Max Rev Len'], max_rev_len_df_rnn['Accuracy'], marker='o')\n",
    "# plt.title('Accuracy for Different Max Rev Len')\n",
    "# plt.xlabel('Max Rev Len')\n",
    "# plt.ylabel('Accuracy')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aa446603",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-10T01:38:32.915772Z",
     "iopub.status.busy": "2024-07-10T01:38:32.915461Z",
     "iopub.status.idle": "2024-07-10T01:38:32.919554Z",
     "shell.execute_reply": "2024-07-10T01:38:32.918595Z"
    },
    "papermill": {
     "duration": 0.059954,
     "end_time": "2024-07-10T01:38:32.921478",
     "exception": false,
     "start_time": "2024-07-10T01:38:32.861524",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Cnn vs rnn\n",
    "# Plot CNN results (green)\n",
    "# plt.figure(figsize=(10, 6))\n",
    "# plt.plot(max_rev_len_df_cnn['Max Rev Len'], max_rev_len_df_cnn['Accuracy'], marker='o', color='green', label='CNN')\n",
    "# plt.title('Accuracy for Different Max Rev Len')\n",
    "# plt.xlabel('Max Rev Len')\n",
    "# plt.ylabel('Accuracy')\n",
    "\n",
    "# Plot RNN results (red)\n",
    "# plt.plot(max_rev_len_df_rnn['Max Rev Len'], max_rev_len_df_rnn['Accuracy'], marker='o', color='red', label='RNN')\n",
    "# plt.legend()  # Show legend with labels 'CNN' and 'RNN'\n",
    "\n",
    "# plt.show()"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "nvidiaTeslaT4",
   "dataSources": [
    {
     "datasetId": 5029575,
     "sourceId": 8479542,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30733,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 2538.303313,
   "end_time": "2024-07-10T01:38:36.157609",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-07-10T00:56:17.854296",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}