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from flask import Flask, request, render_template
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import pickle
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from preprocessing import preprocess_text # pastikan file preprocessing.py ada di folder yang sama
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app = Flask(__name__)
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with open('model_SVM.pkl', 'rb') as f:
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model_SVM = pickle.load(f)
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with open('model_NB.pkl', 'rb') as f:
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model_NB = pickle.load(f)
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def classify_text(processed_text):
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result_svm = model_SVM.predict([processed_text])[0]
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result_nb = model_NB.predict([processed_text])[0]
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return result_svm, result_nb
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@app.route('/', methods=['GET', 'POST'])
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def index():
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input_text = ''
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processed_steps = {}
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result_svm = ''
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result_nb = ''
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tfidf_dict = {}
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if request.method == 'POST':
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input_text = request.form['text']
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processed_steps = preprocess_text(input_text)
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# Gunakan hasil akhir (stemming) untuk prediksi dan TF-IDF
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final_text = processed_steps.get("stemming", "")
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result_svm, result_nb = classify_text(final_text)
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# Dapatkan nilai TF-IDF dari vectorizer di pipeline model_SVM
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vectorizer = model_SVM.named_steps['vectorizer']
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tfidf_vector = vectorizer.transform([final_text])
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dense = tfidf_vector.todense().tolist()[0]
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features = vectorizer.get_feature_names_out()
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tfidf_dict = {feature: round(value, 3) for feature, value in zip(features, dense) if value > 0}
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return render_template('index.html',
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input_text=input_text,
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processed_steps=processed_steps,
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result_svm=result_svm,
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result_nb=result_nb,
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tfidf_dict=tfidf_dict)
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if __name__ == '__main__':
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app.run(debug=True)
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import re
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from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
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def clean_twitter_data(text):
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# Fungsi cleansing: menghilangkan mention, hashtag, RT, URL, dan karakter non-alfanumerik
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text = re.sub(r'@[A-Za-z0-9_]+', '', text)
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text = re.sub(r'#\w+', '', text)
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text = re.sub(r'RT[\s]+', '', text)
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text = re.sub(r'https?://\S+', '', text)
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text = re.sub(r'[^A-Za-z0-9 ]', '', text)
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text = re.sub(r'\s+', ' ', text).strip()
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return text
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def normalize_text(text):
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kamus_normalisasi = {
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'bi': 'bank',
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'ri': 'indonesia',
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'akn': 'akan',
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'gmn': 'bagaimana',
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'ga': 'tidak',
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'gak': 'tidak',
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'nggak': 'tidak',
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'yg': 'yang',
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'kalo': 'kalau',
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'aja': 'saja',
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'nih': 'ini',
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'dong': '',
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'banget': 'sangat',
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'bro': 'teman',
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'sis': 'teman',
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'dgn': 'dengan',
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'bgt': 'sangat',
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'blm': 'belum',
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'jgn': 'jangan',
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'tdk': 'tidak',
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}
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words = text.split()
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normalized_words = [kamus_normalisasi.get(word, word) for word in words]
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return ' '.join(normalized_words)
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def convert_negation(text):
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negation_words = ['tidak', 'bukan', 'tak', 'jangan', 'belum']
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tokens = text.split()
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new_tokens = []
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negate = False
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negation_word = ''
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for token in tokens:
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if token in negation_words:
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negate = True
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negation_word = token
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elif negate:
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new_tokens.append(f"{negation_word}-{token}")
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negate = False
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else:
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new_tokens.append(token)
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return ' '.join(new_tokens)
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def remove_stopwords(text):
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stopwords = set([
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'yang', 'di', 'ke', 'dari', 'dan', 'atau', 'itu', 'ini', 'dengan',
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'pada', 'untuk', 'ada', 'sangat', 'dalam', 'oleh', 'karena'
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])
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words = text.split()
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filtered_words = [word for word in words if word.lower() not in stopwords]
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return ' '.join(filtered_words)
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def stemming(tokenized_text):
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factory = StemmerFactory()
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stemmer = factory.create_stemmer()
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stemmed_words = [stemmer.stem(w) for w in tokenized_text]
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return " ".join(stemmed_words)
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def preprocess_text(text):
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steps = {}
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steps["original_text"] = text
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# 1. Case Folding: konversi ke huruf kecil
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case_folding = text.lower()
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steps["case_folding"] = case_folding
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# 2. Cleansing: hapus mention, hashtag, URL, dll.
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cleansing = clean_twitter_data(case_folding)
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steps["cleansing"] = cleansing
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# 4. Normalization: normalisasi kata dengan kamus
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normalization = normalize_text(cleansing)
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steps["normalization"] = normalization
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# 3. Convert Negation: ubah kata negasi
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negation_converted = convert_negation(normalization)
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steps["convert_negation"] = negation_converted
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# 5. Stopwords: hilangkan stopwords
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no_stopwords = remove_stopwords(negation_converted)
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steps["stopwords"] = no_stopwords
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# 6. Tokenizing: pecah teks jadi token
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tokenizing = no_stopwords.split()
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steps["tokenizing"] = tokenizing
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# 7. Stemming: proses stemming tiap token
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stemming_result = stemming(tokenizing)
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steps["stemming"] = stemming_result
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return steps
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Sentimen Analysis Lengkap</title>
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<script src="https://cdn.tailwindcss.com"></script>
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</head>
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<body class="bg-gray-100">
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<div class="container mx-auto p-6">
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<h1 class="text-3xl font-bold text-center text-blue-600 mb-6">
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Sentimen Analysis dengan SVM & Naive Bayes
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</h1>
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<form method="post" class="bg-white p-6 rounded-lg shadow-md">
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<div class="mb-4">
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<textarea name="text" rows="4" class="w-full p-2 border rounded-lg"
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placeholder="Masukkan teks disini...">{{ input_text }}</textarea>
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</div>
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<button type="submit" class="w-full bg-blue-500 text-white py-2 rounded-lg hover:bg-blue-600">
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Analisis
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</button>
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</form>
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{% if input_text %}
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<!-- Hasil Prediksi -->
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<div class="mt-6 flex space-x-6">
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<div class="flex-1 bg-white p-4 rounded-lg shadow-md border border-gray-300">
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<h3 class="text-lg font-semibold text-center">Hasil Naive Bayes</h3>
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<p class="text-xl text-center text-blue-500 mt-2">{{ result_nb }}</p>
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</div>
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<div class="flex-1 bg-white p-4 rounded-lg shadow-md border border-gray-300">
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<h3 class="text-lg font-semibold text-center">Hasil SVM</h3>
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<p class="text-xl text-center text-blue-500 mt-2">{{ result_svm }}</p>
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</div>
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</div>
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<!-- Detail Proses Preprocessing -->
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<div class="mt-8">
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<h2 class="text-2xl font-bold mb-4 text-center">Detail Proses Preprocessing</h2>
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{% set step_keys = ["case_folding", "cleansing", "normalization", "convert_negation", "stopwords", "tokenizing", "stemming"] %}
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<div class="flex flex-wrap items-center justify-center gap-4">
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{% for key in step_keys %}
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{% if processed_steps[key] is defined %}
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<div class="bg-white p-4 rounded-lg shadow-md border border-gray-300">
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<h3 class="font-semibold capitalize">{{ key | replace('_', ' ') }}</h3>
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{% if processed_steps[key] is iterable and processed_steps[key] is not string %}
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<p>{{ processed_steps[key] | join(', ') }}</p>
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{% else %}
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<p>{{ processed_steps[key] }}</p>
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{% endif %}
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</div>
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{% if not loop.last %}
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<div class="text-3xl font-bold">→</div>
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{% endif %}
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{% endif %}
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{% endfor %}
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</div>
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</div>
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<!-- Nilai TF-IDF -->
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<div class="mt-8">
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<h2 class="text-2xl font-bold mb-4 text-center">Nilai TF-IDF</h2>
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{% if tfidf_dict %}
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<div class="overflow-x-auto">
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<table class="min-w-full bg-white border border-gray-300">
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<thead>
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<tr>
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<th class="px-4 py-2 border">Fitur</th>
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<th class="px-4 py-2 border">Nilai</th>
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</tr>
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</thead>
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<tbody>
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{% for feature, value in tfidf_dict.items() %}
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<tr>
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<td class="px-4 py-2 border">{{ feature }}</td>
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<td class="px-4 py-2 border">{{ value }}</td>
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</tr>
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{% endfor %}
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</tbody>
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</table>
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</div>
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{% else %}
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<p class="text-center">Tidak ada nilai TF-IDF untuk ditampilkan.</p>
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{% endif %}
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</div>
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{% endif %}
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</div>
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</body>
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</html>
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