Comparison K-Means and Fuzzy C-Means In Regencies/Cities Grouping Based on Educational Indicators - Gerald Claudio Messakh; Memi Nor Hayati; Sifriyani Sifriyani (Universitas Bumigora, 2023)
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 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 standard deviation ratio so it can be used efficiently and effectively for decision making by the government to advance the level of education on the island of Kalimantan. 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.