Analisis Kepuasan Masyarakat terhadap Pelayanan Publik menggunakan K-Means Clustering - Yenik Hariyanto; Ardhin Primadewi; Mukhtar Hanafi (Journal of Information System Research (JOSH), 2025)
This study aims to analyze data clustering using the K-Means Clustering method in order to understand certain patterns contained in public satisfaction data on public services. The problem of this study focuses on how to optimally group data to evaluate the quality of service indicators based on 9 indicators in the Public Satisfaction Survey (SKM). The purpose of this study is to divide data into several clusters so that it can provide a clear picture of the differences in quality between service groups. The method used in this study is the K-Means Clustering method, which consists of several stages, namely determining the number of clusters, determining the initial center point, calculating the distance of data to the center point, grouping data, updating the center point, and providing cluster labels. Evaluation of the quality of clustering results is carried out using two evaluation metrics, namely the Silhouette Score and the Davies-Bouldin Index. The results showed that the data was divided into two clusters with a Silhouette Score value of 0.515 which indicated a fairly good clustering quality. In addition, the Davies-Bouldin Index value of 0.784 indicates that the clusters formed have a fairly good distance between each other. The results of this analysis provide an overview that the first cluster has a higher quality of service compared to the second cluster based on the average value of the service indicators measured. This study is useful in providing more structured and accurate information regarding service quality, so that it can be a basis for policy makers to improve service performance in the future. In addition, this study can also be a reference for further research in the application of the K-Means method for similar cases with a focus on evaluation and development of public services.