Analisis Kinerja Decision Tree dan Naïve Bayes Pada Klasifikasi Tingkat Kepuasan Masyarakat - Nora Fitaria; Dedi Darwis (Building of Informatics, Technology and Science (BITS), 2026)
The Public Satisfaction Survey (SKM) is an official instrument used by the government to evaluate public service performance as stipulated in Regulation of the Minister of State Apparatus Empowerment and Bureaucratic Reform (PermenPANRB) Number 14 of 2017. However, the use of SKM data in many government agencies is still limited to calculating satisfaction index values without further predictive analysis. This study aims to classify the level of satisfaction of service users of the Metro City Investment and Integrated Services Agency (DPMPTSP) using the Decision Tree and Naïve Bayes algorithms. The data used is SKM data from 2025 to the fourth quarter, consisting of 2,760 respondents, which consists of nine service elements (U1–U9) with satisfaction categories as class variables. The research process includes data pre-processing, classification modeling using RapidMiner, and model evaluation based on confusion matrix, accuracy, precision, and recall. The results showed that the Naïve Bayes algorithm produced an accuracy rate of 95.04%, higher than the Decision Tree, which obtained an accuracy of 84.46%, and had a better recall value in the dominant class (recall of the Satisfied class was 98.16%). These advantages demonstrate the efficiency of the Naïve Bayes probabilistic approach in handling categorical features in public service elements. This study proves that the application of Data mining techniques to SKM data can support data-based public service evaluation.