The Application of Naive Bayes in Analyzing Public Sentiment Toward the Performance of the North Sumatra Regional Government in Handling Flash Floods

Siburian, Rivaldo
Tampubolon, Rikki Josua
Surbakti, Valentino
Haris, M. Irvandy
Rahmansyah, Rizky

Formosa Journal of Computer and Information Science
Vol. 5 No. 1 (2026): March 2026

This study analyzes public sentiment towards the performance of the North Sumatra Regional Government in handling flash floods using the Multinomial Naive Bayes algorithm. A total of 1,132 opinion data points were collected from social media and news portals through web crawling from November 2025 to February 2026. Sentiment labeling was performed using a lexicon-based approach with the InSet dictionary. Classification results showed a dominance of negative sentiment at 88.4%, focusing on slow emergency response. Model evaluation with an 80:20 data split yielded 89.43% accuracy and an F1-Score of 0.844 for Naive Bayes, while SVM achieved the highest F1-Score (0.855). This study concludes that AI-based sentiment analysis can serve as an objective instrument for government performance auditing.