PERFORMA ALGORITMA K-MEANS DAN FUZZY C-MEANS DALAM ANALISIS KLASTER PENDIDIKAN DI TINGKAT KECAMATAN JAKARTA

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Published: Oct 20, 2023
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K-Means, Fuzzy C-Means, Clustering, Education, Jakarta
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Naramia Wijaya

Abstract

Quality education is a crucial factor in human resource development. However, the quality of education in Indonesia varies significantly, especially at the district level. This study aims to explore and compare the performance of the K-Means and Fuzzy C-Means algorithms in clustering education data at the district level in Jakarta. The dataset used in this study includes various variables related to education quality in Jakarta. The methods employed are K-Means Clustering and Fuzzy C-Means providing more stable and sensitive results to data changes. Based on this comparison, Fuzzy C-Means outperforms K-Means in terms of cluster quality, although both provide valuable insights into understanding education patterns at the district level. This study concludes that the use of clustering algorithms can assist in analyzing and improving education quality in Indonesia, particularly in Jakarta.

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Vol. 18 No. 2 (2023): Jurnal Komputer dan Informatika
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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