ANALISIS SENTIMEN MASYARAKAT PADA KOMENTAR INSTAGRAM TERHADAP PROGRAM PEMERINTAH KOTA PALEMBANG DALAM PENCAPAIAN SDG 6 MENGGUNAKAN ALGORITMA NAÏVE BAYES - Keisha Angelina Tompunu; Ari Wedhasmara (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
Sustainable development is the main focus of local governments in their efforts to improve community welfare, particularly through the achievement of Sustainable Development Goals (SDGs). One of these goals, SDG 6 (Clean Water and Sanitation), emphasizes the importance of access to clean water and proper sanitation for all. This study aims to analyze public sentiment towards the Palembang City Government's program in achieving SDG 6 based on comments on the Instagram platform. This study uses a quantitative approach with text mining and Natural Language Processing (NLP) techniques. The dataset used consists of 5,881 Instagram comments collected through crawling and scraping processes. The research stages include data pre-processing (cleaning, case folding, normalization, tokenizing, stopword removal, and stemming), sentiment labeling (positive, negative, and neutral), and classification using the Multinomial Naïve Bayes algorithm. The test results showed an accuracy rate of 80%, with the highest precision value in the negative class at 0.89 and the highest recall value in the neutral class at 0.97. Sentiment distribution shows that the majority of comments are neutral, reflecting the public's informative perception of issues related to clean water, sanitation, and flooding. The dominance of negative sentiment reflects continued public dissatisfaction with clean water services and drainage infrastructure, while positive comments show appreciation for improvements in government services. The results of this study confirm that social media sentiment analysis can be used as an evaluative tool to measure public perception and monitor progress toward achieving SDG 6 at the local level.