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Klasifikasi_Teks_FNN.ipynb
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Klasifikasi_Teks_FNN.ipynb
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f4a1399a-f23d-4060-a07e-bce5a5c7ddac",
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"metadata": {
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"id": "f4a1399a-f23d-4060-a07e-bce5a5c7ddac"
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},
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"source": [
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"# Klasifikasi Teks menggunakan FNN\n",
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"## Lusiana Situmorang\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "53a214ae-c9cf-4d46-925d-068f1685537b",
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"metadata": {
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"id": "53a214ae-c9cf-4d46-925d-068f1685537b"
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},
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"outputs": [],
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"source": [
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"# =========================\n",
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"# 1. IMPORT LIBRARY\n",
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"# =========================\n",
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"import re\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.metrics import accuracy_score, classification_report\n",
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"\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Dense, Dropout\n",
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"from tensorflow.keras.utils import to_categorical"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"# =========================\n",
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"# 2. DATA TEKS MANUAL\n",
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"# =========================\n",
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"texts = [\n",
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" \"saya suka belajar machine learning\",\n",
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" \"data science sangat menarik\",\n",
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" \"saya tidak suka matematika\",\n",
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" \"python mudah dipelajari\",\n",
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" \"machine learning membutuhkan data\",\n",
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" \"saya benci debugging error\",\n",
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" \"belajar python menyenangkan\",\n",
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" \"matematika penting dalam data science\"\n",
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"]\n",
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"\n",
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"labels = [\n",
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" \"positif\",\n",
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" \"positif\",\n",
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" \"negatif\",\n",
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" \"positif\",\n",
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" \"netral\",\n",
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" \"negatif\",\n",
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" \"positif\",\n",
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" \"netral\"\n",
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"]"
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],
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"metadata": {
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"id": "YUpHatB8LATR"
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},
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"id": "YUpHatB8LATR",
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "9f7d90fe-4af4-446c-9547-c9312bfa6fc7",
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"metadata": {
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"id": "9f7d90fe-4af4-446c-9547-c9312bfa6fc7"
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},
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"outputs": [],
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"source": [
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"# =========================\n",
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"# 3. PREPROCESSING TEKS\n",
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"# =========================\n",
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"def clean_text(text):\n",
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" text = text.lower()\n",
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" text = re.sub(r\"[^a-z\\s]\", \"\", text)\n",
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" text = re.sub(r\"\\s+\", \" \", text).strip()\n",
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" return text\n",
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"\n",
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"texts = [clean_text(t) for t in texts]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "d4b9a7c2-0f08-43fd-8da8-018d839a4917",
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"metadata": {
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"id": "d4b9a7c2-0f08-43fd-8da8-018d839a4917"
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},
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"outputs": [],
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"source": [
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"# =========================\n",
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"# 4. ENCODING LABEL\n",
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"# =========================\n",
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"le = LabelEncoder()\n",
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"y = le.fit_transform(labels)\n",
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"y = to_categorical(y)"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"# =========================\n",
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"# 5. FEATURE EXTRACTION (TF-IDF)\n",
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"# =========================\n",
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"vectorizer = TfidfVectorizer(max_features=1000)\n",
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"X = vectorizer.fit_transform(texts).toarray()"
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],
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"metadata": {
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"id": "jgBIwoPxLJkw"
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},
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"id": "jgBIwoPxLJkw",
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"execution_count": 7,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# =========================\n",
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"# 6. SPLIT DATA\n",
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"# =========================\n",
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"X_train, X_test, y_train, y_test = train_test_split(\n",
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" X, y, test_size=0.25, random_state=42\n",
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")"
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],
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"metadata": {
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"id": "112p_r_WLMEI"
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},
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"id": "112p_r_WLMEI",
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"execution_count": 8,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# =========================\n",
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"# 7. MODEL FNN (DIMODIFIKASI)\n",
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"# =========================\n",
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"model = Sequential()\n",
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"model.add(Dense(64, activation=\"relu\", input_shape=(X_train.shape[1],)))\n",
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"model.add(Dropout(0.3))\n",
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"model.add(Dense(32, activation=\"relu\"))\n",
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"model.add(Dense(y.shape[1], activation=\"softmax\"))\n",
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"\n",
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"model.compile(\n",
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" optimizer=\"adam\",\n",
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" loss=\"categorical_crossentropy\",\n",
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" metrics=[\"accuracy\"]\n",
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")\n",
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"\n",
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"model.summary()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 329
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},
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"id": "WFemRciMLORN",
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"outputId": "a76a6d80-fce1-4ca7-fcb5-9d6e048b7382"
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},
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"id": "WFemRciMLORN",
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"execution_count": 9,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
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" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
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]
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"\u001b[1mModel: \"sequential\"\u001b[0m\n"
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],
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"text/html": [
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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\">Model: \"sequential\"</span>\n",
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"</pre>\n"
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]
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},
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"metadata": {}
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃\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",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,408\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m99\u001b[0m │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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],
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"text/html": [
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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\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
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"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
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"│ 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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,408</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ 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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ 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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
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"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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"│ 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\">3</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">99</span> │\n",
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"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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"</pre>\n"
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]
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},
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"metadata": {}
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,587\u001b[0m (14.01 KB)\n"
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],
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"text/html": [
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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\">3,587</span> (14.01 KB)\n",
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"</pre>\n"
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]
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},
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"metadata": {}
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,587\u001b[0m (14.01 KB)\n"
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],
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"text/html": [
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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\">3,587</span> (14.01 KB)\n",
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"</pre>\n"
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]
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},
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"metadata": {}
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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],
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"text/html": [
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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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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"</pre>\n"
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]
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},
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"metadata": {}
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"# =========================\n",
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"# 8. TRAINING\n",
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"# =========================\n",
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"history = model.fit(\n",
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" X_train,\n",
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" y_train,\n",
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" epochs=30,\n",
|
||||||
|
" batch_size=4,\n",
|
||||||
|
" validation_split=0.2,\n",
|
||||||
|
" verbose=1\n",
|
||||||
|
")"
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/"
|
||||||
|
},
|
||||||
|
"id": "TQH90ZNrLRBt",
|
||||||
|
"outputId": "40144c0a-ecbd-478b-a35c-30a51c755836"
|
||||||
|
},
|
||||||
|
"id": "TQH90ZNrLRBt",
|
||||||
|
"execution_count": 10,
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"output_type": "stream",
|
||||||
|
"name": "stdout",
|
||||||
|
"text": [
|
||||||
|
"Epoch 1/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.5000 - loss: 1.0428 - val_accuracy: 0.0000e+00 - val_loss: 1.0974\n",
|
||||||
|
"Epoch 2/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.2500 - loss: 1.0820 - val_accuracy: 0.0000e+00 - val_loss: 1.1007\n",
|
||||||
|
"Epoch 3/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.5000 - loss: 1.0417 - val_accuracy: 0.0000e+00 - val_loss: 1.1046\n",
|
||||||
|
"Epoch 4/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - accuracy: 0.7500 - loss: 1.0252 - val_accuracy: 0.0000e+00 - val_loss: 1.1079\n",
|
||||||
|
"Epoch 5/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - accuracy: 0.5000 - loss: 1.0166 - val_accuracy: 0.0000e+00 - val_loss: 1.1109\n",
|
||||||
|
"Epoch 6/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - accuracy: 0.5000 - loss: 0.9857 - val_accuracy: 0.0000e+00 - val_loss: 1.1139\n",
|
||||||
|
"Epoch 7/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - accuracy: 1.0000 - loss: 0.9839 - val_accuracy: 0.0000e+00 - val_loss: 1.1175\n",
|
||||||
|
"Epoch 8/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - accuracy: 1.0000 - loss: 0.9848 - val_accuracy: 0.0000e+00 - val_loss: 1.1218\n",
|
||||||
|
"Epoch 9/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - accuracy: 1.0000 - loss: 0.9697 - val_accuracy: 0.0000e+00 - val_loss: 1.1260\n",
|
||||||
|
"Epoch 10/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - accuracy: 0.7500 - loss: 0.9688 - val_accuracy: 0.0000e+00 - val_loss: 1.1295\n",
|
||||||
|
"Epoch 11/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - accuracy: 1.0000 - loss: 0.9126 - val_accuracy: 0.0000e+00 - val_loss: 1.1330\n",
|
||||||
|
"Epoch 12/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - accuracy: 0.5000 - loss: 1.0176 - val_accuracy: 0.0000e+00 - val_loss: 1.1366\n",
|
||||||
|
"Epoch 13/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - accuracy: 0.7500 - loss: 0.9968 - val_accuracy: 0.0000e+00 - val_loss: 1.1404\n",
|
||||||
|
"Epoch 14/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - accuracy: 0.7500 - loss: 0.9723 - val_accuracy: 0.0000e+00 - val_loss: 1.1439\n",
|
||||||
|
"Epoch 15/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - accuracy: 0.7500 - loss: 0.9863 - val_accuracy: 0.0000e+00 - val_loss: 1.1474\n",
|
||||||
|
"Epoch 16/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - accuracy: 1.0000 - loss: 0.9335 - val_accuracy: 0.0000e+00 - val_loss: 1.1506\n",
|
||||||
|
"Epoch 17/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - accuracy: 0.7500 - loss: 0.9062 - val_accuracy: 0.0000e+00 - val_loss: 1.1541\n",
|
||||||
|
"Epoch 18/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - accuracy: 0.7500 - loss: 0.9250 - val_accuracy: 0.0000e+00 - val_loss: 1.1575\n",
|
||||||
|
"Epoch 19/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - accuracy: 0.7500 - loss: 0.9448 - val_accuracy: 0.0000e+00 - val_loss: 1.1609\n",
|
||||||
|
"Epoch 20/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - accuracy: 0.7500 - loss: 0.8955 - val_accuracy: 0.0000e+00 - val_loss: 1.1640\n",
|
||||||
|
"Epoch 21/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - accuracy: 1.0000 - loss: 0.8694 - val_accuracy: 0.0000e+00 - val_loss: 1.1667\n",
|
||||||
|
"Epoch 22/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - accuracy: 0.5000 - loss: 0.9652 - val_accuracy: 0.0000e+00 - val_loss: 1.1694\n",
|
||||||
|
"Epoch 23/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - accuracy: 0.7500 - loss: 0.8697 - val_accuracy: 0.0000e+00 - val_loss: 1.1723\n",
|
||||||
|
"Epoch 24/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - accuracy: 0.7500 - loss: 0.8764 - val_accuracy: 0.0000e+00 - val_loss: 1.1758\n",
|
||||||
|
"Epoch 25/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - accuracy: 0.7500 - loss: 0.8423 - val_accuracy: 0.0000e+00 - val_loss: 1.1791\n",
|
||||||
|
"Epoch 26/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - accuracy: 0.7500 - loss: 0.7802 - val_accuracy: 0.0000e+00 - val_loss: 1.1823\n",
|
||||||
|
"Epoch 27/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - accuracy: 0.7500 - loss: 0.8458 - val_accuracy: 0.0000e+00 - val_loss: 1.1854\n",
|
||||||
|
"Epoch 28/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.7500 - loss: 0.8357 - val_accuracy: 0.0000e+00 - val_loss: 1.1881\n",
|
||||||
|
"Epoch 29/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - accuracy: 0.7500 - loss: 0.7801 - val_accuracy: 0.0000e+00 - val_loss: 1.1910\n",
|
||||||
|
"Epoch 30/30\n",
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - accuracy: 0.7500 - loss: 0.8469 - val_accuracy: 0.0000e+00 - val_loss: 1.1941\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"source": [
|
||||||
|
"# =========================\n",
|
||||||
|
"# 9. EVALUASI\n",
|
||||||
|
"# =========================\n",
|
||||||
|
"y_pred = model.predict(X_test)\n",
|
||||||
|
"y_pred_class = np.argmax(y_pred, axis=1)\n",
|
||||||
|
"y_true = np.argmax(y_test, axis=1)\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\nAccuracy:\", accuracy_score(y_true, y_pred_class))\n",
|
||||||
|
"print(\"\\nClassification Report:\")\n",
|
||||||
|
"print(classification_report(y_true, y_pred_class, target_names=le.classes_))"
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/"
|
||||||
|
},
|
||||||
|
"id": "KZsJ9UBSLTYe",
|
||||||
|
"outputId": "a1c9f60c-cb11-4ac2-cfe5-b9b541c4444d"
|
||||||
|
},
|
||||||
|
"id": "KZsJ9UBSLTYe",
|
||||||
|
"execution_count": 11,
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"output_type": "stream",
|
||||||
|
"name": "stdout",
|
||||||
|
"text": [
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
|
||||||
|
"\n",
|
||||||
|
"Accuracy: 0.0\n",
|
||||||
|
"\n",
|
||||||
|
"Classification Report:\n",
|
||||||
|
" precision recall f1-score support\n",
|
||||||
|
"\n",
|
||||||
|
" negatif 0.00 0.00 0.00 1.0\n",
|
||||||
|
" netral 0.00 0.00 0.00 0.0\n",
|
||||||
|
" positif 0.00 0.00 0.00 1.0\n",
|
||||||
|
"\n",
|
||||||
|
" accuracy 0.00 2.0\n",
|
||||||
|
" macro avg 0.00 0.00 0.00 2.0\n",
|
||||||
|
"weighted avg 0.00 0.00 0.00 2.0\n",
|
||||||
|
"\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: Recall is ill-defined and being set to 0.0 in labels with no true 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: Recall is ill-defined and being set to 0.0 in labels with no true 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: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
|
||||||
|
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"source": [
|
||||||
|
"# =========================\n",
|
||||||
|
"# 10. UJI KALIMAT BARU\n",
|
||||||
|
"# =========================\n",
|
||||||
|
"test_text = [\"saya suka belajar python\"]\n",
|
||||||
|
"test_text = [clean_text(test_text[0])]\n",
|
||||||
|
"X_new = vectorizer.transform(test_text).toarray()\n",
|
||||||
|
"\n",
|
||||||
|
"prediction = model.predict(X_new)\n",
|
||||||
|
"predicted_label = le.inverse_transform([np.argmax(prediction)])\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"\\nKalimat uji:\", test_text[0])\n",
|
||||||
|
"print(\"Hasil klasifikasi:\", predicted_label[0])"
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/"
|
||||||
|
},
|
||||||
|
"id": "hfiVQ8rGLWZ-",
|
||||||
|
"outputId": "091f87bc-e9ec-4853-d253-32e5d87f4d95"
|
||||||
|
},
|
||||||
|
"id": "hfiVQ8rGLWZ-",
|
||||||
|
"execution_count": 12,
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"output_type": "stream",
|
||||||
|
"name": "stdout",
|
||||||
|
"text": [
|
||||||
|
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step\n",
|
||||||
|
"\n",
|
||||||
|
"Kalimat uji: saya suka belajar python\n",
|
||||||
|
"Hasil klasifikasi: netral\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"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": "3.12.2"
|
||||||
|
},
|
||||||
|
"colab": {
|
||||||
|
"provenance": []
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
Loading…
x
Reference in New Issue
Block a user