<![CDATA[Public service reporting systems in Indonesia face significant challenges in processing large volumes of unstructured citizen feedback efficiently. This study proposes Aspiralytica, a mobile-based citizen report classification system that integrates TF-IDF feature extraction with a Multinomial Naive Bayes (MNB) classifier within a hybrid rule-based and machine learning architecture. The system simultaneously performs three-class sentiment classification (positive, negative, neutral) and five-class intent classification (complaint, appreciation, request, emergency, suggestion), with automated priority level determination and a rule-based sarcasm detection module achieving F1 of 0.8980. Evaluated on an augmented dataset of 1,137 sentiment-labeled and 2,187 intent-labeled Indonesian-language citizen report texts using Stratified 10-Fold Cross-Validation, the proposed MNB model achieved sentiment classification accuracy of 96.59% (F1: 96.59%) and intent classification accuracy of 96.97% (F1: 96.96%). An ablation study confirmed TF-IDF with MNB as the dominant performance driver, and a computational efficiency benchmark empirically justified MNB selection with mean inference latency of 0.3955 ms and throughput of 52,312 requests per second. The system is deployed as a FastAPI backend integrated with a React Native mobile frontend, delivering real-time classification through a citizen-facing interface.]]>
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