Sentiment Analysis and Topic Modeling for Discovering Knowledge in Indonesian Mobile Government Applications

Abstract

The accelerated rate of government applications development in Indonesia has introduced new opportunities and challenges in delivering digital public services. While thousands of apps have been developed, systemic issues ranging from usability flaws to authentication failures persist, as reflected in user reviews on platforms like the Google Play Store. This study adopts a knowledge discovery approach to extract actionable insights from more than 17,000 user-generated reviews across three major government applications: Satusehat, Digital Korlantas, and M-Paspor. A hybrid methodology is applied, combining RoBERTa-based sentiment classification, BERTopic-based topic modeling, cosine similarity analysis, and qualitative user validation. The findings reveal recurring issues in authentication, interface design, and system responsiveness that span across organizational boundaries. Cross-app topic correlation highlights critical shared pain points such as login failures and unintuitive UI that undermine user trust in e-government services. Mapping these insights onto the SECI knowledge management model, this research contributes both practical recommendations and a replicable analytical framework for public agencies seeking to institutionalize user feedback. By transforming fragmented digital feedback into organizational knowledge, this study supports continuous service improvement and strengthens the foundation for user-centric e-government.