Comparison of K-Means and K-Medoids Clustering for Grouping The Sub-Districts In Bojonegoro Regency Based On Educational Supporting Factors
DOI:
https://doi.org/10.36456/jstat.vol16.no2.a8079Keywords:
Education Equity, Grouping, K-Means, K-MedoidsAbstract
Education in Bojonegoro is currently still uneven. This is because efforts to equalize education that have been carried out have many obstacles. The obstacle that often occurs is that people who are in remote areas and far from urban areas have difficulty accessing education services. Therefore, regional grouping needs to be done so that the Bojonegoro district government can pay attention to regional clusters that need education improvement. This study used the K-Means and K-Medoids methods to group sub-districts in Bojonegoro district based on educational supporting factors. K-Means is one of the unsupervised learning methods used to analyze data by grouping. Meanwhile, K-Medoids is a partition grouping method that groups a set of n objects into a number of k clusters. The data used in this study is secondary data obtained from the Bojonegoro district Education Office in the form of data on education supporting factors which include the number of schools, the number of educators, and the number of learning groups (ROMBEL) in 2022 in each sub-district in Bojonegoro district. From the research results, it was found that the K-Means method was better than the K-Medoids method. The results of grouping using K-Means obtained as many as 5 clusters, cluster 1 consists of 1 sub-district, cluster 2 consists of 7 sub-districts, cluster 3 consists of 1 sub-district, cluster 4 consists of 12 sub-districts and cluster 5 consists of 7 sub-districts. Based on the characteristics of each cluster obtained, it is expected to be used as input for the Education office for equal distribution of education in Bojonegoro district.
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