Penerapan Metode K-Means Dalam Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator Pendidikan

Messakh, Gerald Claudio
Hayati, Memi Nor
Sifriyani, Sifriyani

Eksponensial
Vol. 14 No. 2 (2023)

Cluster analysis is an analysis that aims to classify data based on the similarity of spesific characteristics. Based on the structure, cluster analysis is divided into two, namely hierarchical and non-hierarchical methods. One of the non-hierarchical methods used in this study is K-Means. K-Means is a partition-based non-hierarchical data grouping method. This purpose of this study is to obtain the best results of grouping regencies/cities on the island of Kalimantan based on education indicators using the K-Means method based on the smallest ratio of standard deviation. Based on the results of the analysis, it can be concluded that the best grouping results based on the smallest ratio of standard deviation is 0.6052 which produces optimal clusters of 2 clusters with the first cluster consisting of 14 Regencies/Cities while the second cluster consists of 42 Regencies/Cities on Kalimantan Island