Penerapan Algoritma K-Medoids pada Pengelompokan Wilayah Provinsi di Indonesia Berdasarkan Indikator Pendidikan

Abstract

Cluster analysis has the goal of grouping data that has the same characteristics into the same cluster and data that has different properties will enter into different clusters. K-Medoids is a grouping method using a representative object as the center point (medoids). The k-medoids method was developed to overcome the weakness of the k-means method which is sensitive to outliers because an object with a large value allows it to deviate from the data distribution in size. After grouping using k-medoids, the results of the grouping were validated. The cluster validation method using the Silhoutte Coefficient (SC) is a method that can be used to see the quality and strength of clusters that combine cohesion and separation methods. This study aims to obtain the optimal cluster from the largest SC value and determine the grouping results of the optimal clusters that are formed. This grouping method is applied to data on education indicators in Indonesia in 2020. Based on the results of the analysis, it is found that the optimal cluster is 2 clusters with a SC value of 0.464, where cluster 1 has 14 provinces and cluster 2 has 20 provinces.

Keywords: K-Medoids, Silhoutte Coefficient, Educational Indicators