The Ministry of Public Works and Public Housing (PUPR) launched the SiKasep application (Subsidized Housing Mortgage Information System) to streamline subsidized housing loan applications. This research analyzes user sentiment toward SiKasep using 3,416 Google Play Store reviews through Naïve Bayes classification to provide actionable insights for government digital service improvement. The methodology encompasses data scraping, comprehensive preprocessing addressing Indonesian language challenges (slang normalization and morphological complexity), TF-IDF feature extraction, and Complement Naïve Bayes classification with hyperparameter optimization. The preprocessing pipeline reduced vocabulary sparsity by 47%, while RandomOverSampler addressed significant class imbalance. The Complement Naïve Bayes classifier achieved 75.98% accuracy with balanced performance across sentiment classes (precision: 79%, recall: 76%, F1-score: 76%). Analysis revealed predominantly negative sentiment (52.4%), primarily related to registration and authentication difficulties, including document verification, login functionality, and KTP integration issues. Positive sentiment highlighted user appreciation for core housing services when technical barriers were absent. The findings emphasize the importance of streamlined registration processes and robust technical infrastructure for government digital services. This research contributes to understanding Indonesian e-government user experiences and provides a replicable sentiment analysis framework supporting evidence-based policy development for enhanced digital service delivery.