<![CDATA[Public opinion through user reviews plays an important role in evaluating the quality of digital public services, especially in mobile-based government applications. This study aims to classify sentiment from 5,000 user reviews of the Info BMKG application using machine learning techniques. The text data underwent pre-processing, including lowercasing, cleaning, normalization, stopword removal, and stemming to improve data quality. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), while class imbalance was handled through the application of the Synthetic Minority Oversampling Technique (SMOTE). Two classification models were tested: Logistic Regression and Extreme Gradient Boosting (XGBoost). Tuning of hyperparameters was conducted through Grid Search combined with five-fold cross-validation to find the optimal model configuration. Evaluation results show that Logistic Regression achieved the best performance with 86.4% accuracy and a weighted F1-score of 0.86, while XGBoost achieved 85.7% accuracy. Both models demonstrated consistent ability in detecting positive and negative sentiment. The findings suggest that Logistic Regression is a suitable and reliable approach for sentiment analysis in short-text reviews within digital public service applications.]]>
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