Sentiment Analysis Of Public Reviews Of Public Services At The Dpmptsp Of Bengkulu Province Using The Naive Bayes Method

Khairullah Khairullah (Universitas Muhammadiyah Bengkulu)
Muhammad Husni Rifqo (Universitas Muhammadiyah Bengkulu)
Ujang Juhardi (Universitas Muhammadiyah Bengkulu)
Na’im Mukarrom Nawwar (Universitas Muhammadiyah Bengkulu)

Jurnal Media Computer Science
Vol 5 No 4 (2026): Oktober

This study aims to analyze public sentiment toward public services at DPMPTSP of Bengkulu Province using the Naive Bayes method. The research data were obtained from public reviews collected during the 2025–2026 period and processed using Natural Language Processing (NLP) techniques, including case folding, cleaning, tokenizing, stopword removal, and stemming. Furthermore, the data were represented using the TF-IDF method and classified into positive, negative, and neutral sentiments using the Naive Bayes algorithm. The results indicate that the Naive Bayes method is capable of classifying public review sentiments effectively and providing an overview of public satisfaction and perceptions regarding the services provided. The findings of this study are expected to serve as evaluation material for DPMPTSP of Bengkulu Province in its efforts to improve the quality of public services.