Clustering Optimization Using K-Means with Principal Component Analysis and Mean-Median Based Centroid Initialization (Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam, 2025)
East Java is the province with the second largest population in Indonesia, which results in major challenges in solving poverty and unemployment, so clustering of districts/cities is needed to identify areas that need more attention, especially in certain aspects of welfare. This study uses data on community welfare indicators in East Java Province and the K-Means clustering algorithm combined with PCA and mean-median centroid initialization. From this method, six clusters were formed, with the main focus on increasing economic growth, decreasing unemployment, and improving the quality of life, which in turn can reduce poverty, thus improving overall welfare. The results show that the application of PCA to K-Means is able to improve the quality of clustering results, while centroid initialization with the mean median value can accelerate the convergence process of K-Means, where the PCA formed produces two principal components with a cumulative percentage of variance of 58%. The clustering evaluation resulted in a Silhouette Coefficient value of 0.505 and DBI of 0.601 with 7 iterations.