PERBANDINGAN RANDOM FOREST, NAIVE BAYES, DAN HEURISTIC POUR UNTUK ANALISIS SENTIMEN MBG
DOI:
https://doi.org/10.32699/biner.v5i2.11465Keywords:
Sentiment Analysis, Random Forest, Naive Bayes, Heuristic Pour, SMOTE.Abstract
Program Makan Bergizi Gratis (MBG) merupakan kebijakan intervensi gizi nasional yang memicu beragam respons masyarakat di media sosial, khususnya YouTube. Penelitian ini bertujuan menganalisis sentimen publik terhadap program MBG, membandingkan kinerja Random Forest, Naive Bayes, dan Heuristic Pour, serta mengevaluasi pengaruh Synthetic Minority Oversampling Technique (SMOTE) dalam menangani ketidakseimbangan kelas. Sebanyak 2.278 komentar YouTube periode Agustus 2024–Januari 2025 diproses melalui tahapan cleaning, tokenizing, stopword removal, stemming, dan pembobotan TF-IDF. Evaluasi model dilakukan menggunakan 10-fold cross-validation berdasarkan confusion matrix, Precision, Recall, F1-score, dan Area Under Curve (AUC). Hasil penelitian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi 0,88 dan AUC 0,91. Penerapan SMOTE meningkatkan kemampuan model dalam mendeteksi sentimen positif sebagai kelas minoritas. Distribusi sentimen didominasi sentimen negatif lebih dari 87,5%. Penelitian ini memberikan kontribusi empiris terkait trade-off antara akurasi dan interpretabilitas dalam analisis sentimen berbahasa Indonesia.. Kebaruan penelitian ini terletak pada perbandingan komprehensif pendekatan machine learning dan rule-based dipadukan dengan SMOTE pada analisis sentimen komentar YouTube berbahasa Indonesia terkait kebijakan MBG. Penelitian ini memberikan bukti empiris mengenai efektivitas penanganan ketidakseimbangan kelas dan menjadi referensi dalam pengembangan analisis sentimen untuk evaluasi kebijakan publik
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