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

Ricky Bahari  Hamid; Chandra  Andriansyah; Dana Indra  Sensuse; Sofian  Lusa; Damayanti  Elisabeth; Nadya Safitri

doi:10.52436/1.jutif.2025.6.6.4991

Authors

Ricky Bahari Hamid Computer Science, Universitas Indonesia, Indonesia
Chandra Andriansyah Computer Science, Universitas Indonesia, Indonesia
Dana Indra Sensuse Computer Science, Universitas Indonesia, Indonesia
Sofian Lusa Computer Science, Universitas Indonesia, Indonesia
Damayanti Elisabeth Computer Science, Universitas Indonesia, Indonesia
Nadya Safitri Computer Science, Universitas Indonesia, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.6.4991

Keywords:

BERTopic, e-Government, Knowledge Discovery, RoBERTa, Sentiment Analysis, Topic Modeling

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.

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