Comparison K-Means and Fuzzy C-Means In Regencies/Cities Grouping Based on Educational Indicators

Abstract

Cluster analysis is an analysis that aims to classify data based on the similarity of specific characteristics.The methods used in this research are K-Means and Fuzzy C-Means (FCM). K-Means is a partitionbased non-hierarchical data grouping method. FCM is a clustering technique in which the existenceof each data is determined by the degree of membership. The purpose of this study is to classifyregencies/cities in Kalimantan based on education indicators in 2021 using K-Means and FCM and findout which method is better to use between K-Means and FCM based on the standard deviation ratio soit can be used efficiently and effectively for decision making by the government to advance the levelof education on the island of Kalimantan. Based on the results of the analysis, it’s concluded that KMeans is the better method with the ratio of the standard deviation within a cluster against the standarddeviation between clusters of 0.6052 which produces optimal clusters of 2 clusters, namely the firstcluster consisting of 14 Regencies/Cities, while the second cluster consists of 42 Regencies/Cities inKalimantan.