CLUSTERING MODEL K-MEANS PADA KASUS ANGKA PUTUS SEKOLAH TINGKATAN SEKOLAH DASAR DI PROVINSI JAWA TENGAH - Laila Khoirun Nisa; Tari Fitri Ningsih; Burhanuddin Izzul Salam; Fauzi, Fatkhurokhman; Eny Winaryati (LogicLink, 2024)
Basic education aims to equip children with the basic skills they need to navigate their lives as individuals, elements of society, elements of citizens and also as human beings, and prepare them for higher education in the future. The case of dropping out of school seems to be a problem that cannot be overcome. The impact of dropping out of school if not managed properly will certainly be detrimental, including having an impact on the quality of resources in the future. Therefore, it is necessary to take action to reduce the dropout rate at the elementary school level. This research was conducted using the K-Means clustering algorithm to find out which districts/cities in Central Java have high, medium, and low dropout rates. The results of clustering using the K-Means algorithm through 3 methods obtained an optimal K value of 3, therefore 3 clusters were formed from all 35 regencies/cities in Central Java, cluster 1 with a tendency for high elementary school dropout rates to be 14 regencies/cities, cluster 2 with a moderate trend of dropout rates from elementary school there are 15 regencies/cities, and cluster 3 with a low trend of dropout rates from elementary school there are 6 regencies/cities.