K-means clustering for classifying the quality management of secondary education in Indonesia

Abstract

Mapping educational management is critical for improving national education quality. This study aims to classify Indonesian provinces in terms of secondary education management within quality standard parameters. Framed in a quantitative approach with the K-means cluster analysis, this study applied eleven parameters to classify the provinces into three main clusters. These parameters were schools, new entrants, students, repeaters, drop-outs, graduates, teachers, classrooms, laboratories, libraries, and school health services. Secondary data related to secondary education in Indonesia published by the Ministry of Education and Culture were analyzed. With the R software, the clusters were formed based on the shared characteristics of the provinces. This study found that Cluster 1 had twenty-two provinces, Cluster 2 two provinces, and Cluster 3 ten provinces. Cluster 2 was found to be the best cluster, while West Java and East Java shared similar characteristics, hence in the same cluster. Further, 5.88% of the provinces were eligible to be pilot models for the standard quality management of education in Indonesia. There is, therefore, a pressing need for the improvement of education infrastructure to support a better-quality education.

Algoritma K-Means Clustering Penggunaan Bandwidth Internet (Studi Kasus di Pemerintah Daerah Kabupaten Padang Pariaman)

“…Saat ini, penelitian KDD mencakup berbagai macam metode penemuan pengetahuan di banyak bidang dan telah mampu menemukan berbagai jenis pengetahuan seperti aturan deret waktu, aturan asosiasi, aturan klasifikasi, dan aturan clustering. Penemuan pengetahuan dalam basis Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) data (KDD) adalah proses penentuan informasi dan pola yang berguna dalam data [2].…”