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Siagian, Tania Annisa
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Computer Science & IT Control & Systems Engineering

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Clustering of the Best Senior High Schools in Serdang Bedagai Regency Using the K-Means Method Siagian, Tania Annisa; Nurdin, Nurdin; Ula, Munirul
Jurnal Sistem Komputer dan Informatika (JSON) Vol 6, No 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8669

Abstract

This study aims to cluster the best Senior High Schools (SMA) in Serdang Bedagai Regency using the K-Means method. Five evaluation indicators were used in the clustering process: accreditation, school status, number of teachers, achievements, and facilities. A total of 41 schools were analyzed using a non-hierarchical approach, with the optimal number of clusters determined through the Elbow Method, resulting in three groups: excellent, good, and fair. Data normalization was performed using the Min-Max method to ensure equal scaling among variables. The clustering results using the K-Means algorithm formed three clusters that represent the quality of schools based on transformed numerical data. The K-Means method proved capable of providing a general overview of school quality grouping, which can serve as a basis for policy-making to improve the quality of education in the region.
Clustering of the Best Senior High Schools in Serdang Bedagai Regency Using the K-Means Method Siagian, Tania Annisa; Nurdin, Nurdin; Ula, Munirul
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8669

Abstract

This study aims to cluster the best Senior High Schools (SMA) in Serdang Bedagai Regency using the K-Means method. Five evaluation indicators were used in the clustering process: accreditation, school status, number of teachers, achievements, and facilities. A total of 41 schools were analyzed using a non-hierarchical approach, with the optimal number of clusters determined through the Elbow Method, resulting in three groups: excellent, good, and fair. Data normalization was performed using the Min-Max method to ensure equal scaling among variables. The clustering results using the K-Means algorithm formed three clusters that represent the quality of schools based on transformed numerical data. The K-Means method proved capable of providing a general overview of school quality grouping, which can serve as a basis for policy-making to improve the quality of education in the region.
Co-Authors Munirul Ula Nurdin Nurdin Nurdin Nurdin