Sentiment Analysis of Public Service Using Naïve Bayes Classifier

• Published in: Journal of Information Systems and Informatics
• Main Authors: Arga Aditia Purnama, Yoannes Romando Sipayung
• Format: Article
• Language: English
• Published: Informatics Department, Faculty of Computer Science Bina Darma University 2025-09-01
• Subjects: sentiment analysis, text mining, naïve bayes, public service quality
• Online Access: https://journal-isi.org/index.php/isi/article/view/1207
• ISSN: 2656-5935; 2656-4882

Public administrative service quality is a crucial factor in citizen satisfaction. This study analyzes sentiment in public service reviews using a text mining approach with the Naïve Bayes Classifier method. The dataset was collected from citizen feedback on online platforms regarding public administrative services. Preprocessing steps included tokenization, case folding, stopword removal, and stemming. The Naïve Bayes algorithm with Laplace smoothing was applied for classification, and performance was evaluated using accuracy, precision, recall, and F1-score. The experiment resulted in an accuracy of 91.2%, precision of 90.3%, recall of 89.7%, and F1-score of 90.0%. The analysis revealed that Service Speed obtained an average score of 3.21, indicating a moderate level of citizen satisfaction in that aspect. These findings suggest that while the Naïve Bayes method is effective for sentiment classification, its greatest value lies in providing actionable insights for public service improvement. Specifically, policymakers can prioritize addressing delays in service speed through simplified procedures, improved staffing, and digital innovation, while maintaining strengths such as officer politeness and effective complaint handling. By leveraging sentiment analysis, public institutions can continuously monitor citizen feedback, identify problem areas, and implement evidence-based strategies to enhance service quality and strengthen public trust.