Perbandingan Metode K-Means, K-Medoids, dan Fuzzy C Means dalam Pengelompokan Kota dan Kabupaten di Provinsi Sumatera Utara Berdasarkan Indikator Indeks Pembangunan Manusia Tahun 2023
DOI:
https://doi.org/10.47662/farabi.v8i1.900Kata Kunci:
Cluster Analysis, Fuzzy C-Means Method, K-Means Method, K-Medoids MethodAbstrak
In March 2023, the percentage of poor people in urban areas was 8.23% and in rural areas was 8.03%, with a decrease of 0.40 points in urban areas and an increase of 0.07 points in rural areas compared to September 2022. Based on these facts, it can be concluded that there has not been even development between villages and city. Cluster analysis is a tool that can be used to group cities and regencies in North Sumatra Province. This research analyzes the comparison of the K-Means, K-Medoids and Fuzzy C-Means methods in conducting cluster analysis. Where in the K-Means method, cluster 1 has 4 cities/regencies, cluster 2 has 8 cities/regencies and cluster 3 has 21 cities/regencies. In the K-Medoids method, the cluster results are the same as the cluster results of the K-Means method. Meanwhile, in the Fuzzy C-Means method, Cluster 1 has 9 cities/regencies, cluster 2 has 16 cities/regencies and cluster 3 has 8 cities/regencies. Then, based on the Davies Bouldin Index (DBI) values from the three methods, it was concluded that the cluster results of the K-Means method were more optimal compared to the K-Medoids and Fuzzy C-Means methods because they had the smallest DBI value.
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