Utilizing IndoBERT and BERTopic to Explore Public Opinion on BPS Instagram Posts - Anugrah, Ahmad Farhan; Agatha, Rendy Dwi (Politeknik Negeri Batam, 2025)
This study aims to analyze public sentiment and topics of opinion toward the Central Statistics Agency (BPS) through comments on the Instagram account @bps_statistics. A total of 3,075 comments collected from January 1 to July 24, 2025, were analyzed using the IndoBERT model for sentiment classification and BERTopic for topic modeling. The IndoBERT model was developed using a semi-supervised learning approach, achieving an 88% classification accuracy with high precision and recall across all sentiment categories. The analysis results show that neutral comments dominate (52.78%), followed by negative comments (31.54%) and positive comments (15.69%). Topic modeling on negative sentiment revealed two main issues: distrust of poverty data and preference for international institution indicators such as the World Bank. Positive sentiment reflects appreciation for the quality of statistical data and moral support for BPS. Neutral comments mostly contain informative discussions about socioeconomic conditions and access to digital services. These findings emphasize the importance of improving BPS public communication, particularly in bridging the gap in public perception of official data. The social media-based approach has proven effective as a complement to formal surveys in capturing public opinion in a broad and dynamic manner.