Analysis and Prediction of MyTelkomsel Complaints Using IndoBERTweet and TimesFM-3 for Service Improvement Prioritization
DOI:
https://doi.org/10.47709/brilliance.v6i4.9879Keywords:
BERTopic, Complaint Forecasting, IndoBERTweet, MyTelkomsel, Sentiment Analysis, TimesFM-3Abstract
Application reviews contain useful feedback, but the growing volume of Indonesian-language reviews makes manual identification of complaints inefficient, and earlier MyTelkomsel studies stopped at sentiment classification. Objective: This study develops an integrated framework to identify the dominant technical complaints in MyTelkomsel reviews, forecast their trends, and prioritize service improvements. Methods: A total of 51,112 application-related Google Play reviews, published between November 2022 and September 2026, were kept after keyword-based relevance filtering. IndoBERTweet, fine-tuned on the SmSA dataset, performed sentiment analysis, and 44,937 negative reviews were grouped by BERTopic with K-Means into ten complaint categories. TimesFM-3 then produced zero-shot six-month complaint forecasting for each category, which was evaluated with a rolling-origin backtest and used to compute a priority score from forecast load and trend. Results: Login & Account Access, Loading/Performance Issues, and General Bugs/Errors accounted for 55.4% of the negative reviews. After two surge months were excluded, TimesFM-3 reached a weighted absolute percentage error of 54.65% and an 80% interval coverage of 84.42%. Loading/Performance Issues was the only high-priority category (score 74.5), and four categories were medium priority. Problems After Updates was the only medium-priority category with an increasing trend. Conclusion: The framework supports ranking and monitoring of complaint categories. Its moderate accuracy and wide intervals mean that the forecasts should guide priorities rather than predict exact monthly volumes.
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Copyright (c) 2026 Muhammad Syaikh Azka, Apriansyah Putra, Rahmat Izwan Heroza, Ken Ditha Tania
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