Analisis Sentimen Masyarakat Terhadap Program Gratis Pol Di TikTok Menggunakan Algoritma Naive bayes - Nandhita Helda Widayani; Eka Arriyanti; Yulindawati (Bulletin of Information Technology (BIT), 2026)
The Gratis Pol Program is an educational initiative in East Kalimantan that has garnered public attention and sparked a wide range of reactions on social media, particularly TikTok. The characteristic use of informal language, abbreviations, and colloquial expressions in TikTok comments poses a challenge for sentiment analysis, necessitating a method capable of systematically classifying public opinion. This study aims to analyze public sentiment toward the Gratis Pol Program based on TikTok user comments using the Naive Bayes algorithm. The research was conducted through the stages of text preprocessing, sentiment labeling using a lexicon-based approach, feature representation using TF-IDF, and the classification process using the Naive Bayes algorithm. The research data was obtained from 13 selected TikTok videos with a total of 1,528 comments, divided into 80% training data and 20% test data. The results show that positive sentiment dominates with 722 comments, followed by 496 neutral comments and 310 negative comments. The classification model achieved an accuracy of 56%, with a macro average F1-score of 0.44 and a weighted average F1-score of 0.48. This study contributes to understanding public perception of the Gratis Pol Program and demonstrates the application of the Naive Bayes algorithm in analyzing the sentiment of social media comments that possess certain characteristics.