<![CDATA[This research discusses sentiment analysis of user comments on the Gobis Suroboyo application using the Naive Bayes algorithm and Logistic Regression. Data was obtained through web scraping method from Google Play Store, with a total of 1,015 comments which then went through text pre-processing such as data cleaning, case folding, stemming, normalisation, filtering, tokenizing, and feature selection using TF-IDF. Sentiment labels were determined based on user ratings, with ratings above 3 as positive and 3 and below as negative. The results show that the Naive Bayes algorithm is better at classifying positive sentiment with a precision of 81% and f1-score of 77%, while Logistic Regression excels at negative sentiment with a precision of 82% and f1-score of 82%. The WordCloud visualisation shows dominant words such as “app”, “good”, and “bus stop” that reflect users attention to the app features and transportation services. The findings show that both algorithms have competitive and reliable performance for evaluating public opinion on comment-based digital services. This research is expected to be a reference for app developers and local governments in improving the quality of digital public services.]]>
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