CLUSTERING DATA SISWA PUTUS SEKOLAH DENGAN ALGORITMA K-MEANS DAN DBSCAN
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Abstract
Human resource development and the progress of a country depend on primary education. Although the enrollment rate of students in primary school has increased rapidly, the dropout phenomenon at this level is still a major problem in Indonesia. This study uses K-Means clustering and DBScan algorithms to cluster data on the number of dropouts in each city in Indonesia. The dataset used comes from the Ministry of Education and Culture published in 2023. This dataset has city/district variables, the number of elementary school dropouts, the number of junior high school dropouts, the number of high school dropouts, and the number of vocational school dropouts. The method that has the best results is the K-Means algorithm with a value of K=2 with a silhouette value of 0.722. In general, the clustering results show that there are 40 regions with high dropout rates, relatively less than the 474 regions with low dropout rates. Although the difference is quite significant, it may indicate that there is an education gap between regions that results in a sizable difference in values. This research is expected to provide important information for stakeholders.
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