From 0af3226a9f590bc5ab7f085918ed9d27b14dc88c Mon Sep 17 00:00:00 2001 From: 202210715016 ALYA PRISCILLA PUTRI <202210715016@mhs.ubharajaya.ac.id> Date: Wed, 21 Jan 2026 01:47:53 +0700 Subject: [PATCH] Upload files to "/" --- klasifikasi_teks_FNN.ipynb | 521 +++++++++++++++++++++++++++++++++++++ 1 file changed, 521 insertions(+) create mode 100644 klasifikasi_teks_FNN.ipynb diff --git a/klasifikasi_teks_FNN.ipynb b/klasifikasi_teks_FNN.ipynb new file mode 100644 index 0000000..a59196d --- /dev/null +++ b/klasifikasi_teks_FNN.ipynb @@ -0,0 +1,521 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wOCzKrPTS65M" + }, + "outputs": [], + "source": [ + "{\n", + " \"cells\": [\n", + " {\n", + " \"cell_type\": \"markdown\",\n", + " \"id\": \"f4a1399a-f23d-4060-a07e-bce5a5c7ddac\",\n", + " \"metadata\": {\n", + " \"id\": \"f4a1399a-f23d-4060-a07e-bce5a5c7ddac\"\n", + " },\n", + " \"source\": [\n", + " \"# Klasifikasi Teks menggunakan FNN\\n\",\n", + " \"## Alya Priscilla Putri\\n\"\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": 3,\n", + " \"id\": \"53a214ae-c9cf-4d46-925d-068f1685537b\",\n", + " \"metadata\": {\n", + " \"id\": \"53a214ae-c9cf-4d46-925d-068f1685537b\"\n", + " },\n", + " \"outputs\": [],\n", + " \"source\": [\n", + " \"# =========================\\n\",\n", + " \"# 1. IMPORT LIBRARY\\n\",\n", + " \"# =========================\\n\",\n", + " \"import re\\n\",\n", + " \"import numpy as np\\n\",\n", + " \"import pandas as pd\\n\",\n", + " \"\\n\",\n", + " \"from sklearn.model_selection import train_test_split\\n\",\n", + " \"from sklearn.feature_extraction.text import TfidfVectorizer\\n\",\n", + " \"from sklearn.preprocessing import LabelEncoder\\n\",\n", + " \"from sklearn.metrics import accuracy_score, classification_report\\n\",\n", + " \"\\n\",\n", + " \"from tensorflow.keras.models import Sequential\\n\",\n", + " \"from tensorflow.keras.layers import Dense, Dropout\\n\",\n", + " \"from tensorflow.keras.utils import to_categorical\"\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"# =========================\\n\",\n", + " \"# 2. DATA TEKS MANUAL\\n\",\n", + " \"# =========================\\n\",\n", + " \"texts = [\\n\",\n", + " \" \\\"saya suka belajar machine learning\\\",\\n\",\n", + " \" \\\"data science sangat menarik\\\",\\n\",\n", + " \" \\\"saya tidak suka matematika\\\",\\n\",\n", + " \" \\\"python mudah dipelajari\\\",\\n\",\n", + " \" \\\"machine learning membutuhkan data\\\",\\n\",\n", + " \" \\\"saya benci debugging error\\\",\\n\",\n", + " \" \\\"belajar python menyenangkan\\\",\\n\",\n", + " \" \\\"matematika penting dalam data science\\\"\\n\",\n", + " \"]\\n\",\n", + " \"\\n\",\n", + " \"labels = [\\n\",\n", + " \" \\\"positif\\\",\\n\",\n", + " \" \\\"positif\\\",\\n\",\n", + " \" \\\"negatif\\\",\\n\",\n", + " \" \\\"positif\\\",\\n\",\n", + " \" \\\"netral\\\",\\n\",\n", + " \" \\\"negatif\\\",\\n\",\n", + " \" \\\"positif\\\",\\n\",\n", + " \" \\\"netral\\\"\\n\",\n", + " \"]\"\n", + " ],\n", + " \"metadata\": {\n", + " \"id\": \"YUpHatB8LATR\"\n", + " },\n", + " \"id\": \"YUpHatB8LATR\",\n", + " \"execution_count\": 4,\n", + " \"outputs\": []\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": 5,\n", + " \"id\": \"9f7d90fe-4af4-446c-9547-c9312bfa6fc7\",\n", + " \"metadata\": {\n", + " \"id\": \"9f7d90fe-4af4-446c-9547-c9312bfa6fc7\"\n", + " },\n", + " \"outputs\": [],\n", + " \"source\": [\n", + " \"# =========================\\n\",\n", + " \"# 3. PREPROCESSING TEKS\\n\",\n", + " \"# =========================\\n\",\n", + " \"def clean_text(text):\\n\",\n", + " \" text = text.lower()\\n\",\n", + " \" text = re.sub(r\\\"[^a-z\\\\s]\\\", \\\"\\\", text)\\n\",\n", + " \" text = re.sub(r\\\"\\\\s+\\\", \\\" \\\", text).strip()\\n\",\n", + " \" return text\\n\",\n", + " \"\\n\",\n", + " \"texts = [clean_text(t) for t in texts]\"\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": 6,\n", + " \"id\": \"d4b9a7c2-0f08-43fd-8da8-018d839a4917\",\n", + " \"metadata\": {\n", + " \"id\": \"d4b9a7c2-0f08-43fd-8da8-018d839a4917\"\n", + " },\n", + " \"outputs\": [],\n", + " \"source\": [\n", + " \"# =========================\\n\",\n", + " \"# 4. ENCODING LABEL\\n\",\n", + " \"# =========================\\n\",\n", + " \"le = LabelEncoder()\\n\",\n", + " \"y = le.fit_transform(labels)\\n\",\n", + " \"y = to_categorical(y)\"\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"# =========================\\n\",\n", + " \"# 5. FEATURE EXTRACTION (TF-IDF)\\n\",\n", + " \"# =========================\\n\",\n", + " \"vectorizer = TfidfVectorizer(max_features=1000)\\n\",\n", + " \"X = vectorizer.fit_transform(texts).toarray()\"\n", + " ],\n", + " \"metadata\": {\n", + " \"id\": \"jgBIwoPxLJkw\"\n", + " },\n", + " \"id\": \"jgBIwoPxLJkw\",\n", + " \"execution_count\": 7,\n", + " \"outputs\": []\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"# =========================\\n\",\n", + " \"# 6. SPLIT DATA\\n\",\n", + " \"# =========================\\n\",\n", + " \"X_train, X_test, y_train, y_test = train_test_split(\\n\",\n", + " \" X, y, test_size=0.25, random_state=42\\n\",\n", + " \")\"\n", + " ],\n", + " \"metadata\": {\n", + " \"id\": \"112p_r_WLMEI\"\n", + " },\n", + " \"id\": \"112p_r_WLMEI\",\n", + " \"execution_count\": 8,\n", + " \"outputs\": []\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"# =========================\\n\",\n", + " \"# 7. 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+        "              \"┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\\n\",\n",
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+        "              \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\",\n",
+        "              \"│ dropout (Dropout)               │ (None, 64)             │             0 │\\n\",\n",
+        "              \"├─────────────────────────────────┼────────────────────────┼───────────────┤\\n\",\n",
+        "              \"│ dense_1 (Dense)                 │ (None, 32)             │         2,080 │\\n\",\n",
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+        "              \"│ dense_2 (Dense)                 │ (None, 3)              │            99 │\\n\",\n",
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+        "              \"
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TRAINING\\n\",\n", + " \"# =========================\\n\",\n", + " \"history = model.fit(\\n\",\n", + " \" X_train,\\n\",\n", + " \" y_train,\\n\",\n", + " \" epochs=30,\\n\",\n", + " \" batch_size=4,\\n\",\n", + " \" validation_split=0.2,\\n\",\n", + " \" verbose=1\\n\",\n", + " \")\"\n", + " ],\n", + " \"metadata\": {\n", + " \"colab\": {\n", + " \"base_uri\": \"https://localhost:8080/\"\n", + " },\n", + " \"id\": \"TQH90ZNrLRBt\",\n", + " \"outputId\": \"40144c0a-ecbd-478b-a35c-30a51c755836\"\n", + " },\n", + " \"id\": \"TQH90ZNrLRBt\",\n", + " \"execution_count\": 10,\n", + " \"outputs\": [\n", + " {\n", + " \"output_type\": \"stream\",\n", + " \"name\": \"stdout\",\n", + " \"text\": [\n", + " \"Epoch 1/30\\n\",\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\",\n", + " \"Epoch 2/30\\n\",\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\",\n", + " \"Epoch 3/30\\n\",\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\",\n", + " \"Epoch 4/30\\n\",\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\",\n", + " \"Epoch 5/30\\n\",\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\",\n", + " \"Epoch 6/30\\n\",\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\",\n", + " \"Epoch 7/30\\n\",\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\",\n", + " \"Epoch 8/30\\n\",\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\",\n", + " \"Epoch 9/30\\n\",\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\",\n", + " \"Epoch 10/30\\n\",\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\",\n", + " \"Epoch 11/30\\n\",\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\",\n", + " \"Epoch 12/30\\n\",\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\",\n", + " \"Epoch 13/30\\n\",\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\",\n", + " \"Epoch 14/30\\n\",\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\",\n", + " \"Epoch 15/30\\n\",\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\",\n", + " \"Epoch 16/30\\n\",\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\",\n", + " \"Epoch 17/30\\n\",\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\",\n", + " \"Epoch 18/30\\n\",\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\",\n", + " \"Epoch 19/30\\n\",\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\",\n", + " \"Epoch 20/30\\n\",\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\",\n", + " \"Epoch 21/30\\n\",\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\",\n", + " \"Epoch 22/30\\n\",\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\",\n", + " \"Epoch 23/30\\n\",\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\",\n", + " \"Epoch 24/30\\n\",\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\",\n", + " \"Epoch 25/30\\n\",\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\",\n", + " \"Epoch 26/30\\n\",\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\",\n", + " \"Epoch 27/30\\n\",\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\",\n", + " \"Epoch 28/30\\n\",\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\",\n", + " \"Epoch 29/30\\n\",\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\",\n", + " \"Epoch 30/30\\n\",\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\"\n", + " ]\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"# =========================\\n\",\n", + " \"# 9. EVALUASI\\n\",\n", + " \"# =========================\\n\",\n", + " \"y_pred = model.predict(X_test)\\n\",\n", + " \"y_pred_class = np.argmax(y_pred, axis=1)\\n\",\n", + " \"y_true = np.argmax(y_test, axis=1)\\n\",\n", + " \"\\n\",\n", + " \"print(\\\"\\\\nAccuracy:\\\", accuracy_score(y_true, y_pred_class))\\n\",\n", + " \"print(\\\"\\\\nClassification Report:\\\")\\n\",\n", + " \"print(classification_report(y_true, y_pred_class, target_names=le.classes_))\"\n", + " ],\n", + " \"metadata\": {\n", + " \"colab\": {\n", + " \"base_uri\": \"https://localhost:8080/\"\n", + " },\n", + " \"id\": \"KZsJ9UBSLTYe\",\n", + " \"outputId\": \"a1c9f60c-cb11-4ac2-cfe5-b9b541c4444d\"\n", + " },\n", + " \"id\": \"KZsJ9UBSLTYe\",\n", + " \"execution_count\": 11,\n", + " \"outputs\": [\n", + " {\n", + " \"output_type\": \"stream\",\n", + " \"name\": \"stdout\",\n", + " \"text\": [\n", + " \"\\u001b[1m1/1\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 76ms/step\\n\",\n", + " \"\\n\",\n", + " \"Accuracy: 0.0\\n\",\n", + " \"\\n\",\n", + " \"Classification Report:\\n\",\n", + " \" precision recall f1-score support\\n\",\n", + " \"\\n\",\n", + " \" negatif 0.00 0.00 0.00 1.0\\n\",\n", + " \" netral 0.00 0.00 0.00 0.0\\n\",\n", + " \" positif 0.00 0.00 0.00 1.0\\n\",\n", + " \"\\n\",\n", + " \" accuracy 0.00 2.0\\n\",\n", + " \" macro avg 0.00 0.00 0.00 2.0\\n\",\n", + " \"weighted avg 0.00 0.00 0.00 2.0\\n\",\n", + " \"\\n\"\n", + " ]\n", + " },\n", + " {\n", + " \"output_type\": \"stream\",\n", + " \"name\": \"stderr\",\n", + " \"text\": [\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\",\n", + " \" _warn_prf(average, modifier, f\\\"{metric.capitalize()} is\\\", len(result))\\n\",\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\",\n", + " \" _warn_prf(average, modifier, f\\\"{metric.capitalize()} is\\\", len(result))\\n\",\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\",\n", + " \" _warn_prf(average, modifier, f\\\"{metric.capitalize()} is\\\", len(result))\\n\",\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\",\n", + " \" _warn_prf(average, modifier, f\\\"{metric.capitalize()} is\\\", len(result))\\n\",\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\",\n", + " \" _warn_prf(average, modifier, f\\\"{metric.capitalize()} is\\\", len(result))\\n\",\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\",\n", + " \" _warn_prf(average, modifier, f\\\"{metric.capitalize()} is\\\", len(result))\\n\"\n", + " ]\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"source\": [\n", + " \"# =========================\\n\",\n", + " \"# 10. UJI KALIMAT BARU\\n\",\n", + " \"# =========================\\n\",\n", + " \"test_text = [\\\"saya suka belajar python\\\"]\\n\",\n", + " \"test_text = [clean_text(test_text[0])]\\n\",\n", + " \"X_new = vectorizer.transform(test_text).toarray()\\n\",\n", + " \"\\n\",\n", + " \"prediction = model.predict(X_new)\\n\",\n", + " \"predicted_label = le.inverse_transform([np.argmax(prediction)])\\n\",\n", + " \"\\n\",\n", + " \"print(\\\"\\\\nKalimat uji:\\\", test_text[0])\\n\",\n", + " \"print(\\\"Hasil klasifikasi:\\\", predicted_label[0])\"\n", + " ],\n", + " \"metadata\": {\n", + " \"colab\": {\n", + " \"base_uri\": \"https://localhost:8080/\"\n", + " },\n", + " \"id\": \"hfiVQ8rGLWZ-\",\n", + " \"outputId\": \"091f87bc-e9ec-4853-d253-32e5d87f4d95\"\n", + " },\n", + " \"id\": \"hfiVQ8rGLWZ-\",\n", + " \"execution_count\": 12,\n", + " \"outputs\": [\n", + " {\n", + " \"output_type\": \"stream\",\n", + " \"name\": \"stdout\",\n", + " \"text\": [\n", + " \"\\u001b[1m1/1\\u001b[0m \\u001b[32m━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[37m\\u001b[0m \\u001b[1m0s\\u001b[0m 70ms/step\\n\",\n", + " \"\\n\",\n", + " \"Kalimat uji: saya suka belajar python\\n\",\n", + " \"Hasil klasifikasi: netral\\n\"\n", + " ]\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " \"metadata\": {\n", + " \"kernelspec\": {\n", + " \"display_name\": \"Python 3 (ipykernel)\",\n", + " \"language\": \"python\",\n", + " \"name\": \"python3\"\n", + " },\n", + " \"language_info\": {\n", + " \"codemirror_mode\": {\n", + " \"name\": \"ipython\",\n", + " \"version\": 3\n", + " },\n", + " \"file_extension\": \".py\",\n", + " \"mimetype\": \"text/x-python\",\n", + " \"name\": \"python\",\n", + " \"nbconvert_exporter\": \"python\",\n", + " \"pygments_lexer\": \"ipython3\",\n", + " \"version\": \"3.12.2\"\n", + " },\n", + " \"colab\": {\n", + " \"provenance\": []\n", + " }\n", + " },\n", + " \"nbformat\": 4,\n", + " \"nbformat_minor\": 5\n", + "}" + ] + } + ] +} \ No newline at end of file