PERBANDINGAN PERFORMA ALGORITMA K-MEANS, K-MEDOIDS, DAN DBSCAN DALAM PENGGEROMBOLAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR KESEJAHTERAAN MASYARAKAT

Abstract

One of the development orientations in Indonesia is to improve the welfare of society. Therefore, it is important to identify and understand the characteristics of community welfare in each province in order to determine effective and targeted development strategies. Cluster analysis is one of the analyses that can be used to group provinces in Indonesia that have homogeneous characteristics within a cluster. The partition method is the simplest and fundamental approach to cluster analysis, but it can only find clusters with spherical-shaped forms. On the other hand, DBSCAN is a density-based clustering algorithm that can be used to find clusters with arbitrary shapes. In this study, the performance of the K-Means, K-Medoids, and DBSCAN algorithms was compared using data that had been dimensionally reduced using the t-SNE method. The data used was the indicator data of community welfare in the year 2022. The evaluation results of clustering based on the highest Silhouette coefficient (0.917) and the lowest Davies-Bouldin index (0.089) indicate that the best clustering methods are K-Means and DBSCAN with parameters perplexity = 1, minPts = 2, and epsilon = 9. Both methods produce the same result, which is the formation of eight clusters.