Pengelompokkan Provinsi Berdasarkan Kelayakan Ruang Kelas dan Tenaga Kependidikan Sekolah Dasar Menggunakan Algoritma K-Means: Analisi Data Periode 2023-2024

Authors

  • Maidy Tri Wardani Universitas Bina Sarana Informatika
  • Varla Octavia Ramadhani Universitas Bina Sarana Informatika
  • Namira Anggreani Universitas Bina Sarana Informatika
  • Sumanto Universitas Bina Sarana Informatika
  • Andi Diah Kuswanto Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.30606/rjti.v4i2.3389

Keywords:

K-Means Clustering, Pendidikan Dasar, Disparatis Regional, Infrastruktur Pendidikan, Tenaga Kependidikan

Abstract

Abstract 

This study analyzes the disparity in primary education quality across Indonesian regions using K-Means clustering methodology to group 38 provinces based on classroom adequacy indicators and teaching staff availability for the 2023-2024 period. Adopting the CRISP-DM methodology and utilizing datasets from the Ministry of Education, Culture, Research, and Technology, this research reveals significant educational gaps across Indonesian regions. The analysis results show three clusters reflecting educational conditions: Cluster 1 (low category) encompasses 23 provinces (67.6%) dominated by Eastern Indonesia regions such as Papua, Maluku, and other remote areas; Cluster 2 (medium category) consists of 13 provinces (38.2%) including South Sumatra, Riau, and DKI Jakarta; and Cluster 3 (high category) contains only 3 provinces: East Java, West Java, and Central Java. Clustering validity is confirmed through silhouette coefficient with the highest value of 0.795 for Cluster 2. These findings identify structural inequality between Java and outer Java regions, providing empirical foundation for the government to design more targeted educational equity policies, with priority on infrastructure rehabilitation and teaching staff augmentation in disadvantaged areas to achieve national educational justice. The research demonstrates that educational quality distribution in Indonesia remains heavily concentrated in Java, while remote and eastern regions face significant challenges in both physical infrastructure and human resources, requiring comprehensive government intervention strategies for sustainable educational development.

Keywords: K-Means clustering, primary education, regional disparity, educational infrastructure, teaching personnel.

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Published

2025-07-03

How to Cite

[1]
M. T. Wardani, V. O. Ramadhani, N. Anggreani, Sumanto, and A. D. Kuswanto, “Pengelompokkan Provinsi Berdasarkan Kelayakan Ruang Kelas dan Tenaga Kependidikan Sekolah Dasar Menggunakan Algoritma K-Means: Analisi Data Periode 2023-2024”, RJTI, vol. 4, no. 2, pp. 163–172, Jul. 2025.

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