Clustering Regional Educational Performance in Indonesia Using K-Means

Herwindo Bagus  Saputro; M. Mujiya Ulkhaq

Authors

Herwindo Bagus Saputro Department of Accounting, Faculty of Economics and Business, Diponegoro University, Semarang
M. Mujiya Ulkhaq Department of Industrial Engineering, Faculty of Engineering, Diponegoro University, Semarang

Keywords:

clustering, education, Indonesia, k-means

Abstract

This study examines regional disparities in educational development across Indonesia by clustering 38 provinces based on indicators of SDG 4 (Quality Education). Using the k-means algorithm with k = 4, the analysis identifies groups of provinces with similar educational profiles to support evidence-based policymaking. The resulting clusters reveal substantial heterogeneity. Cluster 1 (highly disadvantaged) consists of Papua Pegunungan and Papua Tengah, which exhibit the lowest national performance across schooling and attendance indicators. Cluster 2 (disadvantaged) includes 20 provinces with low to moderate achievement levels, including short average schooling duration and low upper-secondary completion. Cluster 3 (advanced) comprises 15 provinces with relatively strong educational outcomes. Cluster 4 (highly advanced) is represented solely by the Special Region of Yogyakarta, demonstrating markedly superior performance. These findings highlight persistent educational inequality and suggest differentiated policy priorities. Interventions for lagging clusters should focus on improving access, teacher quality, and infrastructure, particularly in remote and disadvantaged regions. By providing an empirically derived typology of provincial education performance, this study contributes to better-targeted strategies for achieving SDG 4 and reducing regional disparities in Indonesia.

References

M. M. Ulkhaq, G. Oggioni & R. Riccardi, “How efficient are schools in South-East Asia? An analysis through OECD PISA 2018 data,” Educational Research and Evaluation, vol. 30, no. 3-4, pp. 212-243, 2025.

M. M. Ulkhaq, G. Oggioni & R. Riccardi, “Two-stage super-efficiency model for measuring efficiency of education in South-East Asia,” Decisions in Economics and Finance, vol. 47, no. 2, pp. 513-534, 2024.

A. Luthfi, W. Poespitohadi, I. M. A. W. Putra, & D. I. Mawarni, “Quality education in achieving the SDGs in Indonesia,” Jurnal Ilmu Sosial dan Ilmu Administrasi Negara (Mediasosian), vol. 9, no. 1, pp. 175-190, 2025.

M. M. Ulkhaq, & A. Adyatama, “Clustering countries according to the World Happiness Report 2019,” Engineering and Applied Science Research, vol. 48, no. 2, pp. 137-150, 2021.

M. M. Ulkhaq, “Clustering countries according to the World Happiness Report,” Statistica & Applicazioni, vol. XVIII, no. 2, pp. 197-220, 2020.

M. M. Ulkhaq, “Clustering countries according to the logistics performance index,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 10, no. 1, pp. 1010-1018, 2023.

A. A. Utami, A. G. Ginanjar, N. Fadlia, I. A. Lubis, & M. M. Ulkhaq, “Using shopping and time attitudes to cluster food shoppers: An empirical finding from Indonesia,” Journal of Physics: Conference Series, vol. 1248, p. 012005, 2019. https://doi.org/10.1088/1742-6596/1284/1/012005

F. S. Minako, M. M. Ulkhaq, A. R. A. Pratiwi, D. ‘Sa Nu, & P. Y. Akshinta, “Clustering internet shoppers: An empirical finding from Indonesia,” Proceedings of the 2019 International Conference on E-business and Mobile Commerce (pp. 35-39). https://doi.org/10.1145/3332324.333232

G. R. Suraya & A. W. Wijayanto, “Comparison of hierarchical clustering, k-means, k-medoids, and fuzzy c-means methods in grouping provinces in Indonesia according to the special index for handling stunting,” Indonesian Journal of Statistics and Its Applications, vol. 6, no. 2, pp. 180-201, 2022.

S. Hanifah & A. H. Primandari, “Implementasi metode k-means clustering dalam pengelompokan kabupaten/kota di Provinsi NTB berdasarkan indikator Pendidikan,” Emerging Statistics and Data Science Journal, vol. 1, no. 3, pp. 378-393, 2023.

Z. Mustakim & R. Kamal, “k-means clustering for classifying the quality management of secondary education in Indonesia,” Cakrawala Pendidikan, vol. 40, no. 3, pp. 725-738, 2021.

BPS (2024a). Angka Partisipasi Sekolah (APS) menurut provinsi dan kelompok umur. From: https://www.bps.go.id/id/statistics-table/2/MjIxMSMy/angka-partisipasi-sekolah--aps--menurut-provinsi-dan-kelompok-umur.html

BPS (2024b). Rata-rata Lama Sekolah. From: https://www.bps.go.id/id/statistics-table/2/NDE1IzI=/-metode-baru-rata-rata-lama-sekolah.html

BPS (2024c). Harapan Lama Sekolah. From: From: https://www.bps.go.id/id/statistics-table/2/NDE3IzI=/-metode-baru--harapan-lama-sekolah--tahun-.html

BPS (2024d). Angka partisipasi murni (APM) menurut provinsi dan jenjang Pendidikan. From: https://www.bps.go.id/id/statistics-table/2/MzA0IzI=/angka-partisipasi-murni--apm--menurut-provinsi-dan-jenjang-pendidikan.html