Feedforward ANN Text classification
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# Kompilasi Materi Praktikum
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## Ganjil 2025/2026
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- NLP
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- Machine Learning
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- Big Data
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- Data Mining
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- Data Management
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75
.virtual_documents/NLP/Fitur_Ekstraksi_BOW.ipynb
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.virtual_documents/NLP/Fitur_Ekstraksi_BOW.ipynb
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# Input jumlah dokumen
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import pandas as pd
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n = int(input("Masukkan jumlah dokumen yang ingin dimasukkan: "))
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# Input teks dokumen satu per satu
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documents = []
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for i in range(n):
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teks = input(f"Masukkan teks untuk dokumen ke-{i+1}: ")
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documents.append(teks)
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print("\n=== Dokumen yang Dimasukkan ===")
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for i, doc in enumerate(documents):
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print(f"Doc {i+1}: {doc}")
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# Tahap Tokenisasi
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tokenized_docs = []
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for doc in documents:
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tokens = doc.lower().split()
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tokenized_docs.append(tokens)
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print("\n=== Hasil Tokenisasi ===")
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for i, tokens in enumerate(tokenized_docs):
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print(f"Doc {i+1}: {tokens}")
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# Pembuatan Corpus
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corpus_all = [word for doc in tokenized_docs for word in doc]
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print("\n=== Corpus Keseluruhan (Semua Kata dari Semua Dokumen) ===")
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print(corpus_all)
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print(f"Jumlah total kata dalam seluruh dokumen: {len(corpus_all)}")
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# Pembuatan Vocabulary
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vocabulary = sorted(set(corpus_all))
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print("\n=== Vocabulary (Kata Unik) ===")
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print(vocabulary)
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print(f"Jumlah kata unik (vocabulary size): {len(vocabulary)}")
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vocabulary = sorted(set(corpus_all))
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print("\n=== Vocabulary (Kata Unik) ===")
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for idx, word in enumerate(vocabulary, start=1):
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print(f"{idx:>2}. {word}")
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print(f"\nJumlah kata unik (vocabulary size): {len(vocabulary)}")
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# Representasi Numerik (Matriks BoW)
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bow_matrix = []
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for doc in tokenized_docs:
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vector = [doc.count(word) for word in vocabulary]
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bow_matrix.append(vector)
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df_bow = pd.DataFrame(bow_matrix, columns=vocabulary)
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df_bow.index = [f"D{i}" for i in range(1, len(documents)+1)] # ubah label indeks jadi D1, D2, D3
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print("\n=== Matriks Bag of Words ===")
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print(df_bow)
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# Membuat Tabel Frekuensi Kata (Total dari seluruh dokumen)
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word_frequencies = df_bow.sum().sort_values(ascending=False).reset_index()
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word_frequencies.columns = ["Kata", "Frekuensi"]
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print("\n=== Tabel Frekuensi Kata (Keseluruhan Dokumen) ===")
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print(word_frequencies)
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print(f"Frekuensi kata: {len(word_frequencies)}")
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84
.virtual_documents/NLP/Klasifikasi Teks FNN.ipynb
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.virtual_documents/NLP/Klasifikasi Teks FNN.ipynb
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# ---------------------------------------------------------
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# Klasifikasi Teks dengan TF-IDF + Feedforward Neural Network
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# ---------------------------------------------------------
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.neural_network import MLPClassifier
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from sklearn.metrics import classification_report, confusion_matrix
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# -----------------------------------------
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# 1. Contoh Dataset
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# -----------------------------------------
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# Anda bisa mengganti dataset ini dengan dataset lain (CSV, JSON, dll)
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data = {
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"text": [
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"Saya suka produk ini, luar biasa",
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"Layanannya buruk, sangat kecewa",
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"Pembelian terbaik yang pernah saya lakukan",
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"Saya benci produk ini, buang-buang uang",
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"Kualitasnya sangat bagus, direkomendasikan",
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"Pengalaman buruk, tidak akan membeli lagi"
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],
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"label": ["positive", "negative", "positive", "negative", "positive", "negative"]
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}
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df = pd.DataFrame(data)
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# -----------------------------------------
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# 2. Split Train & Test
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# -----------------------------------------
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X_train, X_test, y_train, y_test = train_test_split(
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df["text"], df["label"], test_size=0.3, random_state=42
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)
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# -----------------------------------------
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# 3. TF-IDF Vectorization
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# -----------------------------------------
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tfidf = TfidfVectorizer(max_features=5000)
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X_train_tfidf = tfidf.fit_transform(X_train)
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X_test_tfidf = tfidf.transform(X_test)
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# -----------------------------------------
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# 4. Feedforward ANN (MLPClassifier)
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# -----------------------------------------
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model = MLPClassifier(
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hidden_layer_sizes=(256, 64),
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activation='relu',
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solver='adam',
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max_iter=500,
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random_state=42
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)
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model.fit(X_train_tfidf, y_train)
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# -----------------------------------------
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# 5. Evaluasi Model
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# -----------------------------------------
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y_pred = model.predict(X_test_tfidf)
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print("=== Classification Report ===")
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print(classification_report(y_test, y_pred))
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print("=== Confusion Matrix ===")
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print(confusion_matrix(y_test, y_pred))
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# -----------------------------------------
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# 6. Prediksi Teks Baru
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# -----------------------------------------
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sample_text = ["barang bagus luar biasa"]
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sample_text = ["barang buruk, saya kecewa"]
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sample_vec = tfidf.transform(sample_text)
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prediction = model.predict(sample_vec)
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print("\nPrediksi untuk:", sample_text[0])
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print("Hasil:", prediction[0])
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209
.virtual_documents/NLP/N-Gram.ipynb
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.virtual_documents/NLP/N-Gram.ipynb
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from collections import Counter
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from IPython.display import clear_output
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import math
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# 1. Input Kalimat dan Tokenisasi
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kalimat = input("Masukkan kalimat: ").strip()
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# Bersihkan output (khusus lingkungan notebook)
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try:
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clear_output()
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except:
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pass
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print(f"Corpus: {kalimat}")
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# Tokenize
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tokens = kalimat.lower().split()
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print(f"Tokens ({len(tokens)}): {tokens}")
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# 2. Hitung Frekuensi Unigram
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unigram_counts = Counter(tokens)
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total_tokens = sum(unigram_counts.values())
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print("\nFrekuensi Unigram dalam kalimat")
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for pair, count in unigram_counts.items():
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print(f" ('{pair}'): {count}")
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print(f"\nTotal unigram dalam 1 kalimat: {total_tokens}")
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# 3. Hitung Probabilitas Unigram: P(wi) = Count(wi) / Total Kata
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unigram_probabilities = {}
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for word, count in unigram_counts.items():
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prob = count / total_tokens
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unigram_probabilities[word] = prob
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print("\nProbabilitas masing-masing unigram:")
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for word, prob in unigram_probabilities.items():
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print(f" P({word}) = {prob:.2f} ({prob*100:.2f}%)")
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# 4. Hitung Probabilitas Kalimat Keseluruhan (P(kalimat) = P(w1) * P(w2) * ...)
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p_kalimat = 1
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prob_parts = []
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# Loop untuk menghitung probabilitas total dan membangun string rumus detail
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for word in tokens:
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prob_value = unigram_probabilities[word]
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p_kalimat *= prob_value
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# Format: P(word)=prob_value
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prob_parts.append(f"P({word})={prob_value:.2f}")
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# Gabungkan bagian-bagian rumus untuk mendapatkan prob_str detail
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prob_str = " x ".join(prob_parts)
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print("\nProbabilitas Keseluruhan Kalimat (Model Unigram):")
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print(f" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.4f} ({p_kalimat*100:.2f}%)")
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from collections import Counter
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from IPython.display import clear_output
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import math
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# 1. Input Kalimat dan Tokenisasi
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kalimat = input("Masukkan kalimat: ").strip()
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# Bersihkan output (khusus lingkungan notebook)
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try:
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clear_output()
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except:
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pass
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print(f"Corpus: {kalimat}")
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# Tokenisasi
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tokens = kalimat.lower().split()
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print(f"Tokens ({len(tokens)}): {tokens}")
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# 2. Hitung Frekuensi Unigram dan Bigram
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unigram_counts = Counter(tokens)
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bigrams = [(tokens[i], tokens[i+1]) for i in range(len(tokens) - 1)]
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bigram_counts = Counter(bigrams)
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print("\nFrekuensi Bigram dalam kalimat:")
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for pair, count in bigram_counts.items():
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print(f" {pair}: {count}")
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print(f"\nTotal bigram dalam 1 kalimat: {sum(bigram_counts.values())}")
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# 3. Hitung Probabilitas Bigram: P(w2 | w1) = Count(w1,w2) / Count(w1)
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bigram_probabilities = {}
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for (w1, w2), count in bigram_counts.items():
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prob = count / unigram_counts[w1]
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bigram_probabilities[(w1, w2)] = prob
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print("\nProbabilitas masing-masing bigram:")
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for (w1, w2), prob in bigram_probabilities.items():
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print(f" P({w2}|{w1}) = {prob:.2f} ({prob*100:.2f}%)")
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# 4. Hitung Probabilitas Kalimat Keseluruhan (Model Bigram)
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# P(kalimat) = P(w1) * P(w2|w1) * P(w3|w2) * ...
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total_tokens = sum(unigram_counts.values())
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p_w1 = unigram_counts.get(tokens[0], 0) / total_tokens # P(w1)
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p_kalimat = p_w1 # Inisialisasi dengan P(w1)
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prob_str_parts = [f"P({tokens[0]})={p_w1:.2f}"] # Tambahkan P(w1) ke rumus
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for i in range(1, len(tokens)):
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pair = (tokens[i-1], tokens[i])
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p = bigram_probabilities.get(pair, 0)
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p_kalimat *= p
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prob_str_parts.append(f"P({pair[1]}|{pair[0]})={p:.2f}")
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# Gabungkan rumus perkalian untuk ditampilkan
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prob_str = " x ".join(prob_str_parts)
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print("\nProbabilitas Keseluruhan Kalimat (Model Bigram):")
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print(f" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.6f} ({p_kalimat*100:.2f}%)")
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from collections import Counter
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from IPython.display import clear_output
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import math
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# 1. Input Kalimat dan Tokenisasi
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kalimat = input("Masukkan kalimat: ").strip()
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# Bersihkan output (khusus lingkungan notebook)
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try:
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clear_output()
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except:
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pass
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print(f"Corpus: {kalimat}")
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# Tokenisasi
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tokens = kalimat.lower().split()
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print(f"Tokens ({len(tokens)}): {tokens}")
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# 2. Hitung Frekuensi Bigram dan Trigram
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bigrams = [(tokens[i], tokens[i+1]) for i in range(len(tokens) - 1)]
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trigrams = [(tokens[i], tokens[i+1], tokens[i+2]) for i in range(len(tokens) - 2)]
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bigram_counts = Counter(bigrams)
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trigram_counts = Counter(trigrams)
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print("\nFrekuensi Trigram dalam kalimat:")
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for tg, count in trigram_counts.items():
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print(f" {tg}: {count}")
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print(f"\nTotal trigram dalam 1 kalimat: {sum(trigram_counts.values())}")
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# 3. Hitung Probabilitas Trigram: P(w3 | w1, w2) = Count(w1,w2,w3) / Count(w1,w2)
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trigram_probabilities = {}
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for (w1, w2, w3), count in trigram_counts.items():
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# Hindari pembagian dengan nol (jika ada bigram yang tidak muncul)
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if bigram_counts[(w1, w2)] > 0:
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prob = count / bigram_counts[(w1, w2)]
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else:
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prob = 0
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trigram_probabilities[(w1, w2, w3)] = prob
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print("\nProbabilitas masing-masing trigram:")
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for (w1, w2, w3), prob in trigram_probabilities.items():
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print(f" P({w3}|{w1},{w2}) = {prob:.2f} ({prob*100:.2f}%)")
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# Tambahkan perhitungan Unigram Count (dibutuhkan untuk P(w1) dan P(w2|w1))
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unigram_counts = Counter(tokens)
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total_tokens = sum(unigram_counts.values())
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# 4. Hitung Probabilitas Kalimat Keseluruhan (Model Trigram)
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# P(kalimat) = P(w1) * P(w2|w1) * P(w3|w1,w2) * ...
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# a. P(w1)
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p_w1 = unigram_counts.get(tokens[0], 0) / total_tokens if total_tokens > 0 else 0
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# b. P(w2|w1) (Menggunakan Bigram tanpa smoothing)
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if len(tokens) > 1:
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count_w1 = unigram_counts.get(tokens[0], 1) # Hindari pembagian dengan 0
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p_w2_w1 = bigram_counts.get((tokens[0], tokens[1]), 0) / count_w1
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else:
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p_w2_w1 = 1.0 # Jika hanya 1 kata
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p_kalimat = p_w1 * p_w2_w1 # Inisialisasi dengan P(w1) * P(w2|w1)
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# Daftar bagian rumus untuk ditampilkan
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prob_str_parts = [f"P({tokens[0]})={p_w1:.2f}"]
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if len(tokens) > 1:
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prob_str_parts.append(f"P({tokens[1]}|{tokens[0]})={p_w2_w1:.2f}")
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# c. Perkalian Trigram P(wi | wi-2, wi-1) untuk i >= 3
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for i in range(len(tokens) - 2):
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triplet = (tokens[i], tokens[i+1], tokens[i+2])
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p = trigram_probabilities.get(triplet, 0)
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p_kalimat *= p
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prob_str_parts.append(f"P({triplet[2]}|{triplet[0]},{triplet[1]})={p:.2f}")
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prob_str = " x ".join(prob_str_parts)
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print("\nProbabilitas Keseluruhan Kalimat (Model Trigram):")
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print(f" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.6f} ({p_kalimat*100:.2f}%)")
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name = 'Fred'
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# Using the old .format() method:
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print('His name is {var}.'.format(var=name))
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# Using f-strings:
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print(f'His name is {name}.')
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print(f'His name is {name!r}')
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d = {'a':123,'b':456}
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print(f'Address: {d['a']} Main Street')
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d = {'a':123,'b':456}
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print(f"Address: {d['a']} Main Street")
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library = [('Author', 'Topic', 'Pages'), ('Twain', 'Rafting', 601), ('Feynman', 'Physics', 95), ('Hamilton', 'Mythology', 144)]
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for book in library:
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print(f'{book[0]:{10}} {book[1]:{8}} {book[2]:{7}}')
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for book in library:
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print(f'{book[0]:{10}} {book[1]:{10}} {book[2]:.>{7}}') # here .> was added
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from datetime import datetime
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today = datetime(year=2018, month=1, day=27)
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print(f'{today:%B %d, %Y}')
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%%writefile test.txt
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Hello, this is a quick test file.
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This is the second line of the file.
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myfile = open('whoops.txt')
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pwd
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|
||||
|
||||
|
||||
|
||||
|
||||
# Open the text.txt file we created earlier
|
||||
my_file = open('test.txt')
|
||||
|
||||
|
||||
my_file
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# We can now read the file
|
||||
my_file.read()
|
||||
|
||||
|
||||
# But what happens if we try to read it again?
|
||||
my_file.read()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Seek to the start of file (index 0)
|
||||
my_file.seek(0)
|
||||
|
||||
|
||||
# Now read again
|
||||
my_file.read()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Readlines returns a list of the lines in the file
|
||||
my_file.seek(0)
|
||||
my_file.readlines()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
my_file.close()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Add a second argument to the function, 'w' which stands for write.
|
||||
# Passing 'w+' lets us read and write to the file
|
||||
|
||||
my_file = open('test.txt','w+')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Write to the file
|
||||
my_file.write('This is a new first line')
|
||||
|
||||
|
||||
# Read the file
|
||||
my_file.seek(0)
|
||||
my_file.read()
|
||||
|
||||
|
||||
my_file.close() # always do this when you're done with a file
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
my_file = open('test.txt','a+')
|
||||
my_file.write('\nThis line is being appended to test.txt')
|
||||
my_file.write('\nAnd another line here.')
|
||||
|
||||
|
||||
my_file.seek(0)
|
||||
print(my_file.read())
|
||||
|
||||
|
||||
my_file.close()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
%%writefile -a test.txt
|
||||
|
||||
This is more text being appended to test.txt
|
||||
And another line here.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
with open('test.txt','r') as txt:
|
||||
first_line = txt.readlines()[0]
|
||||
|
||||
print(first_line)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
txt.read()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
with open('test.txt','r') as txt:
|
||||
for line in txt:
|
||||
print(line, end='') # the end='' argument removes extra linebreaks
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,145 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Import spaCy and load the language library
|
||||
import spacy
|
||||
nlp = spacy.load('en_core_web_sm')
|
||||
|
||||
# Create a Doc object
|
||||
doc = nlp(u'Tesla is looking at buying U.S. startup for $6 million')
|
||||
|
||||
# Print each token separately
|
||||
for token in doc:
|
||||
print(token.text, token.pos_, token.dep_)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
nlp.pipeline
|
||||
|
||||
|
||||
nlp.pipe_names
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc2 = nlp(u"Tesla isn't looking into startups anymore.")
|
||||
|
||||
for token in doc2:
|
||||
print(token.text, token.pos_, token.dep_)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc2
|
||||
|
||||
|
||||
doc2[0]
|
||||
|
||||
|
||||
type(doc2)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc2[0].pos_
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc2[0].dep_
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
spacy.explain('PROPN')
|
||||
|
||||
|
||||
spacy.explain('nsubj')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Lemmas (the base form of the word):
|
||||
print(doc2[4].text)
|
||||
print(doc2[4].lemma_)
|
||||
|
||||
|
||||
# Simple Parts-of-Speech & Detailed Tags:
|
||||
print(doc2[4].pos_)
|
||||
print(doc2[4].tag_ + ' / ' + spacy.explain(doc2[4].tag_))
|
||||
|
||||
|
||||
# Word Shapes:
|
||||
print(doc2[0].text+': '+doc2[0].shape_)
|
||||
print(doc[5].text+' : '+doc[5].shape_)
|
||||
|
||||
|
||||
# Boolean Values:
|
||||
print(doc2[0].is_alpha)
|
||||
print(doc2[0].is_stop)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc3 = nlp(u'Although commmonly attributed to John Lennon from his song "Beautiful Boy", \
|
||||
the phrase "Life is what happens to us while we are making other plans" was written by \
|
||||
cartoonist Allen Saunders and published in Reader\'s Digest in 1957, when Lennon was 17.')
|
||||
|
||||
|
||||
life_quote = doc3[16:30]
|
||||
print(life_quote)
|
||||
|
||||
|
||||
type(life_quote)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc4 = nlp(u'This is the first sentence. This is another sentence. This is the last sentence.')
|
||||
|
||||
|
||||
for sent in doc4.sents:
|
||||
print(sent)
|
||||
|
||||
|
||||
doc4[6].is_sent_start
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,188 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Import spaCy and load the language library
|
||||
import spacy
|
||||
nlp = spacy.load('en_core_web_sm')
|
||||
|
||||
|
||||
# Create a string that includes opening and closing quotation marks
|
||||
mystring = '"We\'re moving to L.A.!"'
|
||||
print(mystring)
|
||||
|
||||
|
||||
# Create a Doc object and explore tokens
|
||||
doc = nlp(mystring)
|
||||
|
||||
for token in doc:
|
||||
print(token.text, end=' | ')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc2 = nlp(u"We're here to help! Send snail-mail, email support@oursite.com or visit us at http://www.oursite.com!")
|
||||
|
||||
for t in doc2:
|
||||
print(t)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc3 = nlp(u'A 5km NYC cab ride costs $10.30')
|
||||
|
||||
for t in doc3:
|
||||
print(t)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc4 = nlp(u"Let's visit St. Louis in the U.S. next year.")
|
||||
|
||||
for t in doc4:
|
||||
print(t)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
len(doc)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
len(doc.vocab)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc5 = nlp(u'It is better to give than to receive.')
|
||||
|
||||
# Retrieve the third token:
|
||||
doc5[2]
|
||||
|
||||
|
||||
# Retrieve three tokens from the middle:
|
||||
doc5[2:5]
|
||||
|
||||
|
||||
# Retrieve the last four tokens:
|
||||
doc5[-4:]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc6 = nlp(u'My dinner was horrible.')
|
||||
doc7 = nlp(u'Your dinner was delicious.')
|
||||
|
||||
|
||||
# Try to change "My dinner was horrible" to "My dinner was delicious"
|
||||
doc6[3] = doc7[3]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc8 = nlp(u'Apple to build a Hong Kong factory for $6 million')
|
||||
|
||||
for token in doc8:
|
||||
print(token.text, end=' | ')
|
||||
|
||||
print('\n----')
|
||||
|
||||
for ent in doc8.ents:
|
||||
print(ent.text+' - '+ent.label_+' - '+str(spacy.explain(ent.label_)))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
len(doc8.ents)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc9 = nlp(u"Autonomous cars shift insurance liability toward manufacturers.")
|
||||
|
||||
for chunk in doc9.noun_chunks:
|
||||
print(chunk.text)
|
||||
|
||||
|
||||
doc10 = nlp(u"Red cars do not carry higher insurance rates.")
|
||||
|
||||
for chunk in doc10.noun_chunks:
|
||||
print(chunk.text)
|
||||
|
||||
|
||||
doc11 = nlp(u"He was a one-eyed, one-horned, flying, purple people-eater.")
|
||||
|
||||
for chunk in doc11.noun_chunks:
|
||||
print(chunk.text)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
from spacy import displacy
|
||||
|
||||
doc = nlp(u'Apple is going to build a U.K. factory for $6 million.')
|
||||
displacy.render(doc, style='dep', jupyter=True, options={'distance': 110})
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc = nlp(u'Over the last quarter Apple sold nearly 20 thousand iPods for a profit of $6 million.')
|
||||
displacy.render(doc, style='ent', jupyter=True)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc = nlp(u'This is a sentence.')
|
||||
displacy.serve(doc, style='dep')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,107 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# RUN THIS CELL to perform standard imports:
|
||||
import spacy
|
||||
nlp = spacy.load('en_core_web_sm')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Enter your code here:
|
||||
|
||||
with open('../TextFiles/owlcreek.txt') as f:
|
||||
doc = nlp(f.read())
|
||||
|
||||
|
||||
# Run this cell to verify it worked:
|
||||
|
||||
doc[:36]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
len(doc)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
sents = [sent for sent in doc.sents]
|
||||
len(sents)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
print(sents[1].text)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# NORMAL SOLUTION:
|
||||
for token in sents[1]:
|
||||
print(token.text, token.pos_, token.dep_, token.lemma_)
|
||||
|
||||
|
||||
# CHALLENGE SOLUTION:
|
||||
for token in sents[1]:
|
||||
print(f'{token.text:{15}} {token.pos_:{5}} {token.dep_:{10}} {token.lemma_:{15}}')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Import the Matcher library:
|
||||
|
||||
from spacy.matcher import Matcher
|
||||
matcher = Matcher(nlp.vocab)
|
||||
|
||||
|
||||
# Create a pattern and add it to matcher:
|
||||
|
||||
pattern = [{'LOWER': 'swimming'}, {'IS_SPACE': True, 'OP':'*'}, {'LOWER': 'vigorously'}]
|
||||
|
||||
matcher.add('Swimming', None, pattern)
|
||||
|
||||
|
||||
# Create a list of matches called "found_matches" and print the list:
|
||||
|
||||
found_matches = matcher(doc)
|
||||
print(found_matches)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
print(doc[1265:1290])
|
||||
|
||||
|
||||
print(doc[3600:3615])
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
for sent in sents:
|
||||
if found_matches[0][1] < sent.end:
|
||||
print(sent)
|
||||
break
|
||||
|
||||
|
||||
for sent in sents:
|
||||
if found_matches[1][1] < sent.end:
|
||||
print(sent)
|
||||
break
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,107 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Perform standard imports
|
||||
import spacy
|
||||
nlp = spacy.load('en_core_web_sm')
|
||||
|
||||
|
||||
# Create a simple Doc object
|
||||
doc = nlp(u"The quick brown fox jumped over the lazy dog's back.")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Print the full text:
|
||||
print(doc.text)
|
||||
|
||||
|
||||
# Print the fifth word and associated tags:
|
||||
print(doc[4].text, doc[4].pos_, doc[4].tag_, spacy.explain(doc[4].tag_))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
for token in doc:
|
||||
print(f'{token.text:{10}} {token.pos_:{8}} {token.tag_:{6}} {spacy.explain(token.tag_)}')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc = nlp(u'I read books on NLP.')
|
||||
r = doc[1]
|
||||
|
||||
print(f'{r.text:{10}} {r.pos_:{8}} {r.tag_:{6}} {spacy.explain(r.tag_)}')
|
||||
|
||||
|
||||
doc = nlp(u'I read a book on NLP.')
|
||||
r = doc[1]
|
||||
|
||||
print(f'{r.text:{10}} {r.pos_:{8}} {r.tag_:{6}} {spacy.explain(r.tag_)}')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc = nlp(u"The quick brown fox jumped over the lazy dog's back.")
|
||||
|
||||
# Count the frequencies of different coarse-grained POS tags:
|
||||
POS_counts = doc.count_by(spacy.attrs.POS)
|
||||
POS_counts
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
doc.vocab[83].text
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
for k,v in sorted(POS_counts.items()):
|
||||
print(f'{k}. {doc.vocab[k].text:{5}}: {v}')
|
||||
|
||||
|
||||
# Count the different fine-grained tags:
|
||||
TAG_counts = doc.count_by(spacy.attrs.TAG)
|
||||
|
||||
for k,v in sorted(TAG_counts.items()):
|
||||
print(f'{k}. {doc.vocab[k].text:{4}}: {v}')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Count the different dependencies:
|
||||
DEP_counts = doc.count_by(spacy.attrs.DEP)
|
||||
|
||||
for k,v in sorted(DEP_counts.items()):
|
||||
print(f'{k}. {doc.vocab[k].text:{4}}: {v}')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,35 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Perform standard imports
|
||||
import spacy
|
||||
nlp = spacy.load('en_core_web_sm')
|
||||
|
||||
# Import the game script
|
||||
import game
|
||||
|
||||
|
||||
# Enter your text here:
|
||||
text = u"The quick brown fox jumped over the lazy dog's back."
|
||||
|
||||
|
||||
# Make your Doc object and pass it into the scorer:
|
||||
doc = nlp(text)
|
||||
print(game.scorer(doc))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# For practice, visualize your fine-grained POS tags (shown in the third column):
|
||||
print(f"{'TOKEN':{10}} {'COARSE':{8}} {'FINE':{6}} {'DESCRIPTION'}")
|
||||
print(f"{'-----':{10}} {'------':{8}} {'----':{6}} {'-----------'}")
|
||||
|
||||
for token in doc:
|
||||
print(f'{token.text:{10}} {token.pos_:{8}} {token.tag_:{6}} {spacy.explain(token.tag_)}')
|
||||
|
||||
|
||||
|
||||
84
.virtual_documents/NLP/Untitled.ipynb
Normal file
84
.virtual_documents/NLP/Untitled.ipynb
Normal file
@ -0,0 +1,84 @@
|
||||
|
||||
|
||||
|
||||
# ---------------------------------------------------------
|
||||
# Klasifikasi Teks dengan TF-IDF + Feedforward Neural Network
|
||||
# ---------------------------------------------------------
|
||||
|
||||
import pandas as pd
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.feature_extraction.text import TfidfVectorizer
|
||||
from sklearn.neural_network import MLPClassifier
|
||||
from sklearn.metrics import classification_report, confusion_matrix
|
||||
|
||||
# -----------------------------------------
|
||||
# 1. Contoh Dataset
|
||||
# -----------------------------------------
|
||||
# Anda bisa mengganti dataset ini dengan dataset lain (CSV, JSON, dll)
|
||||
|
||||
data = {
|
||||
"text": [
|
||||
"Saya suka produk ini, luar biasa",
|
||||
"Layanannya buruk, sangat kecewa",
|
||||
"Pembelian terbaik yang pernah saya lakukan",
|
||||
"Saya benci produk ini, buang-buang uang",
|
||||
"Kualitasnya sangat bagus, direkomendasikan",
|
||||
"Pengalaman buruk, tidak akan membeli lagi"
|
||||
],
|
||||
"label": ["positive", "negative", "positive", "negative", "positive", "negative"]
|
||||
}
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# -----------------------------------------
|
||||
# 2. Split Train & Test
|
||||
# -----------------------------------------
|
||||
X_train, X_test, y_train, y_test = train_test_split(
|
||||
df["text"], df["label"], test_size=0.3, random_state=42
|
||||
)
|
||||
|
||||
# -----------------------------------------
|
||||
# 3. TF-IDF Vectorization
|
||||
# -----------------------------------------
|
||||
tfidf = TfidfVectorizer(max_features=5000)
|
||||
X_train_tfidf = tfidf.fit_transform(X_train)
|
||||
X_test_tfidf = tfidf.transform(X_test)
|
||||
|
||||
# -----------------------------------------
|
||||
# 4. Feedforward ANN (MLPClassifier)
|
||||
# -----------------------------------------
|
||||
model = MLPClassifier(
|
||||
hidden_layer_sizes=(256, 64),
|
||||
activation='relu',
|
||||
solver='adam',
|
||||
max_iter=500,
|
||||
random_state=42
|
||||
)
|
||||
|
||||
model.fit(X_train_tfidf, y_train)
|
||||
|
||||
# -----------------------------------------
|
||||
# 5. Evaluasi Model
|
||||
# -----------------------------------------
|
||||
y_pred = model.predict(X_test_tfidf)
|
||||
|
||||
print("=== Classification Report ===")
|
||||
print(classification_report(y_test, y_pred))
|
||||
|
||||
print("=== Confusion Matrix ===")
|
||||
print(confusion_matrix(y_test, y_pred))
|
||||
|
||||
# -----------------------------------------
|
||||
# 6. Prediksi Teks Baru
|
||||
# -----------------------------------------
|
||||
sample_text = ["barang bagus luar biasa"]
|
||||
sample_text = ["barang buruk, saya kecewa"]
|
||||
sample_vec = tfidf.transform(sample_text)
|
||||
prediction = model.predict(sample_vec)
|
||||
|
||||
print("\nPrediksi untuk:", sample_text[0])
|
||||
print("Hasil:", prediction[0])
|
||||
|
||||
|
||||
|
||||
|
||||
310
NLP/.ipynb_checkpoints/Fitur_Ekstraksi_BOW-checkpoint.ipynb
Normal file
310
NLP/.ipynb_checkpoints/Fitur_Ekstraksi_BOW-checkpoint.ipynb
Normal file
@ -0,0 +1,310 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "qBYcPYAb059g",
|
||||
"outputId": "9f57b704-da1b-4495-d366-24c30586dc76"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdin",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Masukkan jumlah dokumen yang ingin dimasukkan: 3\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Input jumlah dokumen\n",
|
||||
"import pandas as pd\n",
|
||||
"n = int(input(\"Masukkan jumlah dokumen yang ingin dimasukkan: \"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "mo-yt5Ob1N8j",
|
||||
"outputId": "362ac3e0-d84b-4014-db96-cc3b10ecdb32"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Masukkan teks untuk dokumen ke-1: saya belajar nlp di kampus\n",
|
||||
"Masukkan teks untuk dokumen ke-2: saya suka belajar ai\n",
|
||||
"Masukkan teks untuk dokumen ke-3: mahasiswa belajar data science dan nlp\n",
|
||||
"\n",
|
||||
"=== Dokumen yang Dimasukkan ===\n",
|
||||
"Doc 1: saya belajar nlp di kampus\n",
|
||||
"Doc 2: saya suka belajar ai\n",
|
||||
"Doc 3: mahasiswa belajar data science dan nlp\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Input teks dokumen satu per satu\n",
|
||||
"documents = []\n",
|
||||
"for i in range(n):\n",
|
||||
" teks = input(f\"Masukkan teks untuk dokumen ke-{i+1}: \")\n",
|
||||
" documents.append(teks)\n",
|
||||
"\n",
|
||||
"print(\"\\n=== Dokumen yang Dimasukkan ===\")\n",
|
||||
"for i, doc in enumerate(documents):\n",
|
||||
" print(f\"Doc {i+1}: {doc}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "FkmxRAFq1oDK",
|
||||
"outputId": "62c4508e-1725-4f30-fbdb-4de8072498b2"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"=== Hasil Tokenisasi ===\n",
|
||||
"Doc 1: ['saya', 'belajar', 'nlp', 'di', 'kampus']\n",
|
||||
"Doc 2: ['saya', 'suka', 'belajar', 'ai']\n",
|
||||
"Doc 3: ['mahasiswa', 'belajar', 'data', 'science', 'dan', 'nlp']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Tahap Tokenisasi\n",
|
||||
"tokenized_docs = []\n",
|
||||
"for doc in documents:\n",
|
||||
" tokens = doc.lower().split()\n",
|
||||
" tokenized_docs.append(tokens)\n",
|
||||
"\n",
|
||||
"print(\"\\n=== Hasil Tokenisasi ===\")\n",
|
||||
"for i, tokens in enumerate(tokenized_docs):\n",
|
||||
" print(f\"Doc {i+1}: {tokens}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "ybC1Vo2C_c3q",
|
||||
"outputId": "fa31c57e-5364-4ded-fcd0-54d0db46c34b"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"=== Corpus Keseluruhan (Semua Kata dari Semua Dokumen) ===\n",
|
||||
"['saya', 'belajar', 'nlp', 'di', 'kampus', 'saya', 'suka', 'belajar', 'ai', 'mahasiswa', 'belajar', 'data', 'science', 'dan', 'nlp']\n",
|
||||
"Jumlah total kata dalam seluruh dokumen: 15\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Pembuatan Corpus\n",
|
||||
"corpus_all = [word for doc in tokenized_docs for word in doc]\n",
|
||||
"\n",
|
||||
"print(\"\\n=== Corpus Keseluruhan (Semua Kata dari Semua Dokumen) ===\")\n",
|
||||
"print(corpus_all)\n",
|
||||
"print(f\"Jumlah total kata dalam seluruh dokumen: {len(corpus_all)}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "s6S-Ma4R1xuq",
|
||||
"outputId": "98c3685b-1798-4038-d17e-6e45ca419b51"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"=== Vocabulary (Kata Unik) ===\n",
|
||||
"['ai', 'belajar', 'dan', 'data', 'di', 'kampus', 'mahasiswa', 'nlp', 'saya', 'science', 'suka']\n",
|
||||
"Jumlah kata unik (vocabulary size): 11\n",
|
||||
"\n",
|
||||
"=== Vocabulary (Kata Unik) ===\n",
|
||||
" 1. ai\n",
|
||||
" 2. belajar\n",
|
||||
" 3. dan\n",
|
||||
" 4. data\n",
|
||||
" 5. di\n",
|
||||
" 6. kampus\n",
|
||||
" 7. mahasiswa\n",
|
||||
" 8. nlp\n",
|
||||
" 9. saya\n",
|
||||
"10. science\n",
|
||||
"11. suka\n",
|
||||
"\n",
|
||||
"Jumlah kata unik (vocabulary size): 11\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Pembuatan Vocabulary\n",
|
||||
"vocabulary = sorted(set(corpus_all))\n",
|
||||
"\n",
|
||||
"print(\"\\n=== Vocabulary (Kata Unik) ===\")\n",
|
||||
"print(vocabulary)\n",
|
||||
"print(f\"Jumlah kata unik (vocabulary size): {len(vocabulary)}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"vocabulary = sorted(set(corpus_all))\n",
|
||||
"\n",
|
||||
"print(\"\\n=== Vocabulary (Kata Unik) ===\")\n",
|
||||
"for idx, word in enumerate(vocabulary, start=1):\n",
|
||||
" print(f\"{idx:>2}. {word}\")\n",
|
||||
"print(f\"\\nJumlah kata unik (vocabulary size): {len(vocabulary)}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"id": "ShevCTva2Fg9"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Representasi Numerik (Matriks BoW)\n",
|
||||
"bow_matrix = []\n",
|
||||
"for doc in tokenized_docs:\n",
|
||||
" vector = [doc.count(word) for word in vocabulary]\n",
|
||||
" bow_matrix.append(vector)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "-yB6D2pY2M0E",
|
||||
"outputId": "b6b2f4d3-da8b-4aee-e9ce-034def4d5cf7"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"=== Matriks Bag of Words ===\n",
|
||||
" ai belajar dan data di kampus mahasiswa nlp saya science suka\n",
|
||||
"D1 0 1 0 0 1 1 0 1 1 0 0\n",
|
||||
"D2 1 1 0 0 0 0 0 0 1 0 1\n",
|
||||
"D3 0 1 1 1 0 0 1 1 0 1 0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df_bow = pd.DataFrame(bow_matrix, columns=vocabulary)\n",
|
||||
"df_bow.index = [f\"D{i}\" for i in range(1, len(documents)+1)] # ubah label indeks jadi D1, D2, D3\n",
|
||||
"\n",
|
||||
"print(\"\\n=== Matriks Bag of Words ===\")\n",
|
||||
"print(df_bow)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "8ruf5vKL2rGD",
|
||||
"outputId": "65a4674e-1c01-4833-ec55-f66f77b8b6c2"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"=== Tabel Frekuensi Kata (Keseluruhan Dokumen) ===\n",
|
||||
" Kata Frekuensi\n",
|
||||
"0 belajar 3\n",
|
||||
"1 nlp 2\n",
|
||||
"2 saya 2\n",
|
||||
"3 dan 1\n",
|
||||
"4 ai 1\n",
|
||||
"5 data 1\n",
|
||||
"6 di 1\n",
|
||||
"7 mahasiswa 1\n",
|
||||
"8 kampus 1\n",
|
||||
"9 science 1\n",
|
||||
"10 suka 1\n",
|
||||
"Frekuensi kata: 11\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Membuat Tabel Frekuensi Kata (Total dari seluruh dokumen)\n",
|
||||
"word_frequencies = df_bow.sum().sort_values(ascending=False).reset_index()\n",
|
||||
"word_frequencies.columns = [\"Kata\", \"Frekuensi\"]\n",
|
||||
"\n",
|
||||
"print(\"\\n=== Tabel Frekuensi Kata (Keseluruhan Dokumen) ===\")\n",
|
||||
"print(word_frequencies)\n",
|
||||
"print(f\"Frekuensi kata: {len(word_frequencies)}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "NQjExannHuj0"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": []
|
||||
},
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@ -0,0 +1,6 @@
|
||||
{
|
||||
"cells": [],
|
||||
"metadata": {},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
380
NLP/.ipynb_checkpoints/N-Gram-checkpoint.ipynb
Normal file
380
NLP/.ipynb_checkpoints/N-Gram-checkpoint.ipynb
Normal file
@ -0,0 +1,380 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "JVPdWpz3hhbj"
|
||||
},
|
||||
"source": [
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "4Mvva3v65h1v"
|
||||
},
|
||||
"source": [
|
||||
"# **UNIGRAM**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "1cub_VJnUJMl",
|
||||
"outputId": "1889eb61-4f3f-4780-f42e-02368076cce3"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Corpus: saya suka makan nasi\n",
|
||||
"Tokens (4): ['saya', 'suka', 'makan', 'nasi']\n",
|
||||
"\n",
|
||||
"Frekuensi Unigram dalam kalimat\n",
|
||||
" ('saya'): 1\n",
|
||||
" ('suka'): 1\n",
|
||||
" ('makan'): 1\n",
|
||||
" ('nasi'): 1\n",
|
||||
"\n",
|
||||
"Total unigram dalam 1 kalimat: 4\n",
|
||||
"\n",
|
||||
"Probabilitas masing-masing unigram:\n",
|
||||
" P(saya) = 0.25 (25.00%)\n",
|
||||
" P(suka) = 0.25 (25.00%)\n",
|
||||
" P(makan) = 0.25 (25.00%)\n",
|
||||
" P(nasi) = 0.25 (25.00%)\n",
|
||||
"\n",
|
||||
"Probabilitas Keseluruhan Kalimat (Model Unigram):\n",
|
||||
" P(saya suka makan nasi) = P(saya)=0.25 x P(suka)=0.25 x P(makan)=0.25 x P(nasi)=0.25 = 0.0039 (0.39%)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from collections import Counter\n",
|
||||
"from IPython.display import clear_output\n",
|
||||
"import math\n",
|
||||
"\n",
|
||||
"# 1. Input Kalimat dan Tokenisasi\n",
|
||||
"kalimat = input(\"Masukkan kalimat: \").strip()\n",
|
||||
"\n",
|
||||
"# Bersihkan output (khusus lingkungan notebook)\n",
|
||||
"try:\n",
|
||||
" clear_output()\n",
|
||||
"except:\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"print(f\"Corpus: {kalimat}\")\n",
|
||||
"\n",
|
||||
"# Tokenize\n",
|
||||
"tokens = kalimat.lower().split()\n",
|
||||
"print(f\"Tokens ({len(tokens)}): {tokens}\")\n",
|
||||
"\n",
|
||||
"# 2. Hitung Frekuensi Unigram\n",
|
||||
"unigram_counts = Counter(tokens)\n",
|
||||
"total_tokens = sum(unigram_counts.values())\n",
|
||||
"\n",
|
||||
"print(\"\\nFrekuensi Unigram dalam kalimat\")\n",
|
||||
"for pair, count in unigram_counts.items():\n",
|
||||
" print(f\" ('{pair}'): {count}\")\n",
|
||||
"print(f\"\\nTotal unigram dalam 1 kalimat: {total_tokens}\")\n",
|
||||
"\n",
|
||||
"# 3. Hitung Probabilitas Unigram: P(wi) = Count(wi) / Total Kata\n",
|
||||
"unigram_probabilities = {}\n",
|
||||
"for word, count in unigram_counts.items():\n",
|
||||
" prob = count / total_tokens\n",
|
||||
" unigram_probabilities[word] = prob\n",
|
||||
"\n",
|
||||
"print(\"\\nProbabilitas masing-masing unigram:\")\n",
|
||||
"for word, prob in unigram_probabilities.items():\n",
|
||||
" print(f\" P({word}) = {prob:.2f} ({prob*100:.2f}%)\")\n",
|
||||
"\n",
|
||||
"# 4. Hitung Probabilitas Kalimat Keseluruhan (P(kalimat) = P(w1) * P(w2) * ...)\n",
|
||||
"p_kalimat = 1\n",
|
||||
"prob_parts = []\n",
|
||||
"\n",
|
||||
"# Loop untuk menghitung probabilitas total dan membangun string rumus detail\n",
|
||||
"for word in tokens:\n",
|
||||
" prob_value = unigram_probabilities[word]\n",
|
||||
" p_kalimat *= prob_value\n",
|
||||
" # Format: P(word)=prob_value\n",
|
||||
" prob_parts.append(f\"P({word})={prob_value:.2f}\")\n",
|
||||
"\n",
|
||||
"# Gabungkan bagian-bagian rumus untuk mendapatkan prob_str detail\n",
|
||||
"prob_str = \" x \".join(prob_parts)\n",
|
||||
"\n",
|
||||
"print(\"\\nProbabilitas Keseluruhan Kalimat (Model Unigram):\")\n",
|
||||
"print(f\" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.4f} ({p_kalimat*100:.2f}%)\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Vstwt996-FrS"
|
||||
},
|
||||
"source": [
|
||||
"# **BIGRAM**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "XRIY4qgTVbjl",
|
||||
"outputId": "ea6e62ce-45a0-40c9-ca98-1fcc30558479"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Corpus: saya makan nasi dan saya makan roti\n",
|
||||
"Tokens (7): ['saya', 'makan', 'nasi', 'dan', 'saya', 'makan', 'roti']\n",
|
||||
"\n",
|
||||
"Frekuensi Bigram dalam kalimat:\n",
|
||||
" ('saya', 'makan'): 2\n",
|
||||
" ('makan', 'nasi'): 1\n",
|
||||
" ('nasi', 'dan'): 1\n",
|
||||
" ('dan', 'saya'): 1\n",
|
||||
" ('makan', 'roti'): 1\n",
|
||||
"\n",
|
||||
"Total bigram dalam 1 kalimat: 6\n",
|
||||
"\n",
|
||||
"Probabilitas masing-masing bigram:\n",
|
||||
" P(makan|saya) = 1.00 (100.00%)\n",
|
||||
" P(nasi|makan) = 0.50 (50.00%)\n",
|
||||
" P(dan|nasi) = 1.00 (100.00%)\n",
|
||||
" P(saya|dan) = 1.00 (100.00%)\n",
|
||||
" P(roti|makan) = 0.50 (50.00%)\n",
|
||||
"\n",
|
||||
"Probabilitas Keseluruhan Kalimat (Model Bigram):\n",
|
||||
" P(saya makan nasi dan saya makan roti) = P(saya)=0.29 x P(makan|saya)=1.00 x P(nasi|makan)=0.50 x P(dan|nasi)=1.00 x P(saya|dan)=1.00 x P(makan|saya)=1.00 x P(roti|makan)=0.50 = 0.071429 (7.14%)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from collections import Counter\n",
|
||||
"from IPython.display import clear_output\n",
|
||||
"import math\n",
|
||||
"\n",
|
||||
"# 1. Input Kalimat dan Tokenisasi\n",
|
||||
"kalimat = input(\"Masukkan kalimat: \").strip()\n",
|
||||
"\n",
|
||||
"# Bersihkan output (khusus lingkungan notebook)\n",
|
||||
"try:\n",
|
||||
" clear_output()\n",
|
||||
"except:\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"print(f\"Corpus: {kalimat}\")\n",
|
||||
"\n",
|
||||
"# Tokenisasi\n",
|
||||
"tokens = kalimat.lower().split()\n",
|
||||
"print(f\"Tokens ({len(tokens)}): {tokens}\")\n",
|
||||
"\n",
|
||||
"# 2. Hitung Frekuensi Unigram dan Bigram\n",
|
||||
"unigram_counts = Counter(tokens)\n",
|
||||
"bigrams = [(tokens[i], tokens[i+1]) for i in range(len(tokens) - 1)]\n",
|
||||
"bigram_counts = Counter(bigrams)\n",
|
||||
"\n",
|
||||
"print(\"\\nFrekuensi Bigram dalam kalimat:\")\n",
|
||||
"for pair, count in bigram_counts.items():\n",
|
||||
" print(f\" {pair}: {count}\")\n",
|
||||
"print(f\"\\nTotal bigram dalam 1 kalimat: {sum(bigram_counts.values())}\")\n",
|
||||
"\n",
|
||||
"# 3. Hitung Probabilitas Bigram: P(w2 | w1) = Count(w1,w2) / Count(w1)\n",
|
||||
"bigram_probabilities = {}\n",
|
||||
"for (w1, w2), count in bigram_counts.items():\n",
|
||||
" prob = count / unigram_counts[w1]\n",
|
||||
" bigram_probabilities[(w1, w2)] = prob\n",
|
||||
"\n",
|
||||
"print(\"\\nProbabilitas masing-masing bigram:\")\n",
|
||||
"for (w1, w2), prob in bigram_probabilities.items():\n",
|
||||
" print(f\" P({w2}|{w1}) = {prob:.2f} ({prob*100:.2f}%)\")\n",
|
||||
"\n",
|
||||
"# 4. Hitung Probabilitas Kalimat Keseluruhan (Model Bigram)\n",
|
||||
"# P(kalimat) = P(w1) * P(w2|w1) * P(w3|w2) * ...\n",
|
||||
"total_tokens = sum(unigram_counts.values())\n",
|
||||
"p_w1 = unigram_counts.get(tokens[0], 0) / total_tokens # P(w1)\n",
|
||||
"p_kalimat = p_w1 # Inisialisasi dengan P(w1)\n",
|
||||
"\n",
|
||||
"prob_str_parts = [f\"P({tokens[0]})={p_w1:.2f}\"] # Tambahkan P(w1) ke rumus\n",
|
||||
"\n",
|
||||
"for i in range(1, len(tokens)):\n",
|
||||
" pair = (tokens[i-1], tokens[i])\n",
|
||||
" p = bigram_probabilities.get(pair, 0)\n",
|
||||
" p_kalimat *= p\n",
|
||||
" prob_str_parts.append(f\"P({pair[1]}|{pair[0]})={p:.2f}\")\n",
|
||||
"\n",
|
||||
"# Gabungkan rumus perkalian untuk ditampilkan\n",
|
||||
"prob_str = \" x \".join(prob_str_parts)\n",
|
||||
"\n",
|
||||
"print(\"\\nProbabilitas Keseluruhan Kalimat (Model Bigram):\")\n",
|
||||
"print(f\" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.6f} ({p_kalimat*100:.2f}%)\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "E6n1IU8X-G9S"
|
||||
},
|
||||
"source": [
|
||||
"# **TRIGRAM**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "BIRARsj2FHJg",
|
||||
"outputId": "968d420e-9370-40e5-e7e1-148e1d351d62"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Corpus: mahasiswa mengerjakan tugas kemudian mahasiswa upload e-learning\n",
|
||||
"Tokens (7): ['mahasiswa', 'mengerjakan', 'tugas', 'kemudian', 'mahasiswa', 'upload', 'e-learning']\n",
|
||||
"\n",
|
||||
"Frekuensi Trigram dalam kalimat:\n",
|
||||
" ('mahasiswa', 'mengerjakan', 'tugas'): 1\n",
|
||||
" ('mengerjakan', 'tugas', 'kemudian'): 1\n",
|
||||
" ('tugas', 'kemudian', 'mahasiswa'): 1\n",
|
||||
" ('kemudian', 'mahasiswa', 'upload'): 1\n",
|
||||
" ('mahasiswa', 'upload', 'e-learning'): 1\n",
|
||||
"\n",
|
||||
"Total trigram dalam 1 kalimat: 5\n",
|
||||
"\n",
|
||||
"Probabilitas masing-masing trigram:\n",
|
||||
" P(tugas|mahasiswa,mengerjakan) = 1.00 (100.00%)\n",
|
||||
" P(kemudian|mengerjakan,tugas) = 1.00 (100.00%)\n",
|
||||
" P(mahasiswa|tugas,kemudian) = 1.00 (100.00%)\n",
|
||||
" P(upload|kemudian,mahasiswa) = 1.00 (100.00%)\n",
|
||||
" P(e-learning|mahasiswa,upload) = 1.00 (100.00%)\n",
|
||||
"\n",
|
||||
"Probabilitas Keseluruhan Kalimat (Model Trigram):\n",
|
||||
" P(mahasiswa mengerjakan tugas kemudian mahasiswa upload e-learning) = P(mahasiswa)=0.29 x P(mengerjakan|mahasiswa)=0.50 x P(tugas|mahasiswa,mengerjakan)=1.00 x P(kemudian|mengerjakan,tugas)=1.00 x P(mahasiswa|tugas,kemudian)=1.00 x P(upload|kemudian,mahasiswa)=1.00 x P(e-learning|mahasiswa,upload)=1.00 = 0.142857 (14.29%)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from collections import Counter\n",
|
||||
"from IPython.display import clear_output\n",
|
||||
"import math\n",
|
||||
"\n",
|
||||
"# 1. Input Kalimat dan Tokenisasi\n",
|
||||
"kalimat = input(\"Masukkan kalimat: \").strip()\n",
|
||||
"\n",
|
||||
"# Bersihkan output (khusus lingkungan notebook)\n",
|
||||
"try:\n",
|
||||
" clear_output()\n",
|
||||
"except:\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"print(f\"Corpus: {kalimat}\")\n",
|
||||
"\n",
|
||||
"# Tokenisasi\n",
|
||||
"tokens = kalimat.lower().split()\n",
|
||||
"print(f\"Tokens ({len(tokens)}): {tokens}\")\n",
|
||||
"\n",
|
||||
"# 2. Hitung Frekuensi Bigram dan Trigram\n",
|
||||
"bigrams = [(tokens[i], tokens[i+1]) for i in range(len(tokens) - 1)]\n",
|
||||
"trigrams = [(tokens[i], tokens[i+1], tokens[i+2]) for i in range(len(tokens) - 2)]\n",
|
||||
"\n",
|
||||
"bigram_counts = Counter(bigrams)\n",
|
||||
"trigram_counts = Counter(trigrams)\n",
|
||||
"\n",
|
||||
"print(\"\\nFrekuensi Trigram dalam kalimat:\")\n",
|
||||
"for tg, count in trigram_counts.items():\n",
|
||||
" print(f\" {tg}: {count}\")\n",
|
||||
"print(f\"\\nTotal trigram dalam 1 kalimat: {sum(trigram_counts.values())}\")\n",
|
||||
"\n",
|
||||
"# 3. Hitung Probabilitas Trigram: P(w3 | w1, w2) = Count(w1,w2,w3) / Count(w1,w2)\n",
|
||||
"trigram_probabilities = {}\n",
|
||||
"for (w1, w2, w3), count in trigram_counts.items():\n",
|
||||
" # Hindari pembagian dengan nol (jika ada bigram yang tidak muncul)\n",
|
||||
" if bigram_counts[(w1, w2)] > 0:\n",
|
||||
" prob = count / bigram_counts[(w1, w2)]\n",
|
||||
" else:\n",
|
||||
" prob = 0\n",
|
||||
" trigram_probabilities[(w1, w2, w3)] = prob\n",
|
||||
"\n",
|
||||
"print(\"\\nProbabilitas masing-masing trigram:\")\n",
|
||||
"for (w1, w2, w3), prob in trigram_probabilities.items():\n",
|
||||
" print(f\" P({w3}|{w1},{w2}) = {prob:.2f} ({prob*100:.2f}%)\")\n",
|
||||
"\n",
|
||||
"# Tambahkan perhitungan Unigram Count (dibutuhkan untuk P(w1) dan P(w2|w1))\n",
|
||||
"unigram_counts = Counter(tokens)\n",
|
||||
"total_tokens = sum(unigram_counts.values())\n",
|
||||
"\n",
|
||||
"# 4. Hitung Probabilitas Kalimat Keseluruhan (Model Trigram)\n",
|
||||
"# P(kalimat) = P(w1) * P(w2|w1) * P(w3|w1,w2) * ...\n",
|
||||
"\n",
|
||||
"# a. P(w1)\n",
|
||||
"p_w1 = unigram_counts.get(tokens[0], 0) / total_tokens if total_tokens > 0 else 0\n",
|
||||
"\n",
|
||||
"# b. P(w2|w1) (Menggunakan Bigram tanpa smoothing)\n",
|
||||
"if len(tokens) > 1:\n",
|
||||
" count_w1 = unigram_counts.get(tokens[0], 1) # Hindari pembagian dengan 0\n",
|
||||
" p_w2_w1 = bigram_counts.get((tokens[0], tokens[1]), 0) / count_w1\n",
|
||||
"else:\n",
|
||||
" p_w2_w1 = 1.0 # Jika hanya 1 kata\n",
|
||||
"\n",
|
||||
"p_kalimat = p_w1 * p_w2_w1 # Inisialisasi dengan P(w1) * P(w2|w1)\n",
|
||||
"\n",
|
||||
"# Daftar bagian rumus untuk ditampilkan\n",
|
||||
"prob_str_parts = [f\"P({tokens[0]})={p_w1:.2f}\"]\n",
|
||||
"if len(tokens) > 1:\n",
|
||||
" prob_str_parts.append(f\"P({tokens[1]}|{tokens[0]})={p_w2_w1:.2f}\")\n",
|
||||
"\n",
|
||||
"# c. Perkalian Trigram P(wi | wi-2, wi-1) untuk i >= 3\n",
|
||||
"for i in range(len(tokens) - 2):\n",
|
||||
" triplet = (tokens[i], tokens[i+1], tokens[i+2])\n",
|
||||
" p = trigram_probabilities.get(triplet, 0)\n",
|
||||
" p_kalimat *= p\n",
|
||||
" prob_str_parts.append(f\"P({triplet[2]}|{triplet[0]},{triplet[1]})={p:.2f}\")\n",
|
||||
"\n",
|
||||
"prob_str = \" x \".join(prob_str_parts)\n",
|
||||
"\n",
|
||||
"print(\"\\nProbabilitas Keseluruhan Kalimat (Model Trigram):\")\n",
|
||||
"print(f\" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.6f} ({p_kalimat*100:.2f}%)\")\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": []
|
||||
},
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
151
NLP/Klasifikasi Teks FNN.ipynb
Normal file
151
NLP/Klasifikasi Teks FNN.ipynb
Normal file
@ -0,0 +1,151 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f4a1399a-f23d-4060-a07e-bce5a5c7ddac",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Klasifikasi Teks\n",
|
||||
"## Arif R Dwiyanto"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "53a214ae-c9cf-4d46-925d-068f1685537b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"=== Classification Report ===\n",
|
||||
" precision recall f1-score support\n",
|
||||
"\n",
|
||||
" negative 0.00 0.00 0.00 1.0\n",
|
||||
" positive 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",
|
||||
"=== Confusion Matrix ===\n",
|
||||
"[[0 1]\n",
|
||||
" [1 0]]\n",
|
||||
"\n",
|
||||
"Prediksi untuk: barang buruk, saya kecewa\n",
|
||||
"Hasil: negative\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# ---------------------------------------------------------\n",
|
||||
"# Klasifikasi Teks dengan TF-IDF + Feedforward Neural Network\n",
|
||||
"# ---------------------------------------------------------\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"from sklearn.neural_network import MLPClassifier\n",
|
||||
"from sklearn.metrics import classification_report, confusion_matrix\n",
|
||||
"\n",
|
||||
"# -----------------------------------------\n",
|
||||
"# 1. Contoh Dataset\n",
|
||||
"# -----------------------------------------\n",
|
||||
"# Anda bisa mengganti dataset ini dengan dataset lain (CSV, JSON, dll)\n",
|
||||
"\n",
|
||||
"data = {\n",
|
||||
" \"text\": [\n",
|
||||
" \"Saya suka produk ini, luar biasa\",\n",
|
||||
" \"Layanannya buruk, sangat kecewa\",\n",
|
||||
" \"Pembelian terbaik yang pernah saya lakukan\",\n",
|
||||
" \"Saya benci produk ini, buang-buang uang\",\n",
|
||||
" \"Kualitasnya sangat bagus, direkomendasikan\",\n",
|
||||
" \"Pengalaman buruk, tidak akan membeli lagi\"\n",
|
||||
" ],\n",
|
||||
" \"label\": [\"positive\", \"negative\", \"positive\", \"negative\", \"positive\", \"negative\"]\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"df = pd.DataFrame(data)\n",
|
||||
"\n",
|
||||
"# -----------------------------------------\n",
|
||||
"# 2. Split Train & Test\n",
|
||||
"# -----------------------------------------\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
||||
" df[\"text\"], df[\"label\"], test_size=0.3, random_state=42\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# -----------------------------------------\n",
|
||||
"# 3. TF-IDF Vectorization\n",
|
||||
"# -----------------------------------------\n",
|
||||
"tfidf = TfidfVectorizer(max_features=5000)\n",
|
||||
"X_train_tfidf = tfidf.fit_transform(X_train)\n",
|
||||
"X_test_tfidf = tfidf.transform(X_test)\n",
|
||||
"\n",
|
||||
"# -----------------------------------------\n",
|
||||
"# 4. Feedforward ANN (MLPClassifier)\n",
|
||||
"# -----------------------------------------\n",
|
||||
"model = MLPClassifier(\n",
|
||||
" hidden_layer_sizes=(256, 64),\n",
|
||||
" activation='relu',\n",
|
||||
" solver='adam',\n",
|
||||
" max_iter=500,\n",
|
||||
" random_state=42\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"model.fit(X_train_tfidf, y_train)\n",
|
||||
"\n",
|
||||
"# -----------------------------------------\n",
|
||||
"# 5. Evaluasi Model\n",
|
||||
"# -----------------------------------------\n",
|
||||
"y_pred = model.predict(X_test_tfidf)\n",
|
||||
"\n",
|
||||
"print(\"=== Classification Report ===\")\n",
|
||||
"print(classification_report(y_test, y_pred))\n",
|
||||
"\n",
|
||||
"print(\"=== Confusion Matrix ===\")\n",
|
||||
"print(confusion_matrix(y_test, y_pred))\n",
|
||||
"\n",
|
||||
"# -----------------------------------------\n",
|
||||
"# 6. Prediksi Teks Baru\n",
|
||||
"# -----------------------------------------\n",
|
||||
"sample_text = [\"barang bagus luar biasa\"]\n",
|
||||
"sample_text = [\"barang buruk, saya kecewa\"]\n",
|
||||
"sample_vec = tfidf.transform(sample_text)\n",
|
||||
"prediction = model.predict(sample_vec)\n",
|
||||
"\n",
|
||||
"print(\"\\nPrediksi untuk:\", sample_text[0])\n",
|
||||
"print(\"Hasil:\", prediction[0])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9f7d90fe-4af4-446c-9547-c9312bfa6fc7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user