Cluster analysis of the national examination: School grouping to maintain the sustainability of high school quality

Raoda Ismail, Universitas Cenderawasih, Indonesia
Heri Retnawati, Universitas Negeri Yogyakarta, Indonesia
Okky Riswandha Imawan, Universitas Cenderawasih, Indonesia


This study aims to classify high schools in Papua Province, Indonesia, based on the 2019 National Examination scores so they can be considered in maintaining the sustainability of school quality in Papua. In this study, all senior high schools in Papua Province were grouped into three clusters: Cluster 1 (high), Cluster 2 (medium), and Cluster 3 (low clusters) using the K-Means Algorithm on the 2019 National Examination data. The data were obtained through the website official Center of Educational Assessment of the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia. Clarification was done by grouping data on national examination scores from each school based on the similarity of the data with data from other schools. The results of the high school clustering using the K-Means Algorithm show that 18 schools are in Cluster 1, 58 schools in Cluster 2, and 68 schools in Cluster 3. The results of the analysis of the K-Means Algorithm show an R2 value of 0.723 and a Silhouette score of 0.42.


clusterization; K-means; Euclidean distance; national examination; high school

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