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

  • Gerald Claudio Messakh Universitas Mulawarman, Indonesia
  • Memi Nor Hayati Universitas Mulawarman, Indonesia
  • Sifriyani Sifriyani Universitas Mulawarman, Indonesia
Keywords: Education Indicators, Fuzzy C-Means, K-Means


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 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 KMeans 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


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How to Cite
G. Messakh, M. Hayati, and S. Sifriyani, “Comparison K-Means and Fuzzy C-Means In Regencies/Cities Grouping Based on Educational Indicators”, Jurnal Varian, vol. 7, no. 1, pp. 99 - 114, Oct. 2023.