Comparison of K-Means and Fuzzy C-Means Methods in Regencies/Cities Grouping in Kalimantan Based on Educational Indicators
Abstract
Cluster analysis is an analysis that aims to classify data based on the similarity of specific
characteristics. The clustering methods used in this research are K-Means and Fuzzy C-Means
(FCM). K-Means is a partition-based non-hierarchical data grouping method. FCM is a clustering
technique in which the existence of each data is determined by the degree of membership. The
purpose of this study is to classify regencies/cities in Kalimantan based on education indicators in
2021 using K-Means and FCM and find out which method is better to use between K-Means and
FCM based on the ratio of the standard deviation. Based on the results of the analysis, it's concluded
that K-Means is the better method with the ratio of the standard deviation within a cluster against the
standard deviation between clusters of 0.6052 which produces optimal clusters of 2 clusters, namely
the first cluster consisting of 14 Regencies/Cities, while the second cluster consists of 42
Regencies/Cities in Kalimantan.
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