Comparison of the Performance of the K-Means and K-Medoids Algorithms in Grouping Regencies/Cities in Sumatera Based on Poverty Indicators

Abstract

K-Means is a non-hierarchical approach that separates data into a number of groups according on how far an object is from the closest centroid. K-Medoids is a non-hierarchical clustering technique that separates data into a number of groups according on how far away an object is from the closest medoid. The two approaches were put to the test using data on poverty in Sumatra in 2021, when the Covid-19 outbreak had caused the poverty rate to increase from the year before. This research is an applied research which begins by studying relevant theories. The data used in this study is secondary data sources from the BPS website regarding poverty indicators. This study aims to determine regional groups and compare the results of grouping with the k-means and k-medoids methods. To find out the best performance between the two methods, that is by looking at the lowest Davies Bouldin Index (DBI). The results of this study are the k-means algorithm produces as many as 34 districts/cities incorporated in cluster 1, 52 districts/cities in cluster 2, 23 districts/cities in cluster 3, and 45 districts/cities in cluster 4. k-medoids, namely in clusters 1, 2, 3, and 4, respectively, as many as 53, 40, 37, and 24 districts/cities. Based on the results of the grouping, the DBI k-means of 1,584 and k-medoids of 2,359 were obtained. This means that the k-means algorithm is better than the k-medoids, because the k-means DBI is smaller than the k-medoids.