Analysis of Earthquake Notification Complaint Topics in Info BMKG Reviews Using BERTopic - Hanipah Diniyaturobiah; Rifky Khoerul Muzaky (Aliansi Peneliti Informatika, 2026)
Reliable earthquake notification services in public information applications play a critical role in supporting public awareness and preparedness in seismically active regions. This study examines user complaints about earthquake notification features in the Info BMKG mobile application by analyzing publicly available Google Play Store user reviews. A total of 1,500 reviews were collected and examined, with complaint reviews operationally defined as those with star ratings of 3 or lower. Prior to analysis, the dataset underwent text preprocessing and a balancing procedure to ensure adequate representation of complaint-related content. Topic modeling was conducted using BERTopic, a transformer-based approach that enables context-aware clustering of short, informal text, followed by descriptive temporal analysis to examine variations in complaint occurrence over time. The analytical workflow included text normalization, embedding generation, topic extraction, and temporal mapping of complaint patterns. The results reveal several recurring complaint themes, including delayed or missing notifications, clarity of information, application performance issues, and user responses to system updates. Temporal variations indicate periods of increased complaint activity that align with heightened application usage, reflecting shifts in user engagement rather than direct evidence of system failure. Topic validity was assessed through qualitative inspection of representative reviews to ensure semantic consistency and interpretability. Overall, this study provides a structured, descriptive overview of user concerns regarding earthquake notification services and demonstrates the applicability of topic-level and temporal analysis as an evaluative approach for mobile disaster information applications, without making causal performance claims.