Analisis Sentimen Program Mbg Menggunakan Algoritma Random Forest Dan Naive Bayes - Hidayat, Rahmat; Juliah Ratnaningsih, Dewi (Journal of Computing and Informatics Research, 2025)
Abstrak: Transformasi digital telah mendorong perubahan besar dalam berbagai aspek kehidupan, salah satunya melalui kemunculan teknologi MBG yang memunculkan beragam opini publik. Penelitian ini menganalisis 6.728 komentar masyarakat di media sosial X (Twitter) menggunakan pendekatan text mining untuk menilai sentimen terhadap MBG serta membandingkan performa dua algoritma, yaitu Naïve Bayes dan Logistic Regression. Hasil awal menunjukkan akurasi masing-masing sebesar 90% dan 91%, namun ketidakseimbangan data dengan dominasi sentimen positif menurunkan nilai precision, recall, dan F1-Score. Melalui penerapan metode SMOTE untuk mengatasi ketimpangan data, performa kedua algoritma meningkat, dengan Logistic Regression menunjukkan hasil terbaik (akurasi 95%, precision 94%, recall 93%, dan F1-Score 95%). Temuan ini menunjukkan bahwa Logistic Regression lebih unggul dalam menganalisis sentimen masyarakat terhadap perkembangan teknologi MBG. Kata kunci: Naïve Bayes, Logistic Regression, MBG, Analisis Sentimen. Abstract: Digital transformation has driven significant changes in various aspects of life, one of which is through the emergence of MBG technology, which has generated diverse public opinions. This research analyzes 6,728 public comments on the social media platform X (Twitter) using a text mining approach to assess sentiment towards the MBG and to compare the performance of two algorithms: Naïve Bayes and Logistic Regression. Initial results showed respective accuracies of 90% and 91%, but data imbalance, with a dominance of positive sentiment, lowered the precision, recall, and F1-Score values. Through the application of the SMOTE method to address the data imbalance, the performance of both algorithms improved, with Logistic Regression showing the best results (95% accuracy, 94% precision, 93% recall, and 95% F1-Score). These findings indicate that Logistic Regression is superior in analyzing public sentiment towards the development of MBG technology. Keywords: Naïve Bayes, Logistic Regression, MBG, Sentiment Analysis.