SPATIAL ANALYSIS AND K-MEANS CLUSTERING IN MAPPING EDUCATIONAL PARTICIPATION INEQUALITY ACROSS PROVINCES IN INDONESIA
DOI:
https://doi.org/10.33509/jan.v32i1.3993Kata Kunci:
Educational Participation, Spatial Analysis, K-Means Clustering, Education Policy, Educational EquityAbstrak
Equal access to education is an important indicator in human resource development and reducing regional inequality in Indonesia. Differences in geographical conditions, socio-economic factors, and availability of educational infrastructure may create disparities in educational participation across provinces. This study aims to analyze the pattern of educational participation inequality across provinces in Indonesia and to identify regional groupings based on education participation levels.This research employs a quantitative approach using provincial educational participation data by education level in 2025. The analysis was conducted through descriptive statistical analysis, spatial mapping, and regional clustering using the K-Means clustering method.The results show that participation at the basic education level is relatively evenly distributed, with an average participation rate of 98.06 percent at the primary school level and 94.66 percent at the junior secondary school level. However, participation declines at the senior secondary level to 76.75 percent and decreases more significantly at the higher education level to 29.48 percent. The clustering results identify three regional groups consisting of a high participation group with two provinces, a medium participation group with twenty-four provinces, and a relatively low participation group with twelve provinces. This study contributes theoretically by integrating spatial analysis and unsupervised machine learning in examining educational inequality, thereby advancing the application of computational approaches in public policy analysis. From a policy perspective, the findings provide an empirical basis for data-driven governance and support the formulation of place-based education policies aimed at reducing regional disparities in educational participation.
Unduhan
Referensi
Abdullah, M., & Firmansyah, D. (2021). Regional development inequality and public service distribution in Indonesia. Jurnal Administrasi Publik Indonesia, 7(2), 145–158.
Ananda, R., Yuliani, C. E., Misnati, K., Azzikra, R., & Rahmadiah, P. (2025). Analisis kesenjangan layanan pendidikan sekolah dasar antara sekolah perkotaan dan daerah 3T di Indonesia [Analysis of primary school education service gaps between urban schools and 3T regions in Indonesia]. Jurnal Pendas, 10(1), 87–98.
Anselin, L. (2019). Spatial econometrics: Methods and models. Springer.
Ansell, C., & Gash, A. (2023). Collaborative governance and public policy implementation. Oxford University Press.
Ardianti, R., Memi, M., & Lestari, A. (2025). Ketimpangan pendidikan di Indonesia: Kajian literatur dan wawancara [Educational inequality in Indonesia: A literature review and interviews]. Global Research and Innovation Journal, 1(2), 65–70. (Note: Assumed "Memi" is a first initial/name; adjust if it is a double surname).
Barca, F. (2024). Place-based development policies and regional inequality. European Commission Press.
Darmawan, R., & Laksmi, N. (2023). Public service accessibility and human development disparity in Indonesian provinces. Jurnal Ilmu Administrasi Negara, 19(1), 33–47.
Denhardt, R. B., & Denhardt, J. V. (2021). The new public service: Serving, not steering (Updated ed.). Routledge.
Fadilah, N., & Hartono, B. (2022). Machine learning approach in regional development clustering analysis. Jurnal Statistika Terapan Indonesia, 6(1), 55–67.
Firmansyah, A., Nugroho, T., & Sari, M. (2023). Spatial data mining for regional policy planning in Indonesia. Jurnal Geografi Dan Pembangunan Wilayah, 8(2), 102–116.
Grindle, M. S. (2022). Good governance and institutional capacity in public sector reform. Oxford University Press.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Han, J., Kamber, M., & Pei, J. (2021). Data mining: Concepts and techniques (4th ed.). Morgan Kaufmann.
Handayani, T., & Prabowo, H. (2023). Data-driven governance in public sector decision making. Jurnal Kebijakan Dan Administrasi Publik, 27(1), 1–15.
Haryowidyatna, E., Ichikawa, M., & Fujita, K. (2025). Region-based recommendations for upper secondary school types in Indonesia using clustering analysis. Proceedings of International Conference on Management in Emerging Markets.
Head, B. W. (2022). Evidence-based policymaking: Principles and requirements. Australian Journal of Public Administration, 81(3), 345–362.
Head, B. W. (2023). Evidence-based public policy: Principles and practice. Policy Press.
Hidayat, R., & Nuraini, S. (2022). Educational inequality and regional disparity in Indonesia. Jurnal Pendidikan Dan Kebijakan Publik, 14(2), 120–134.
Hidayat, R., & Sari, N. (2021). The effectiveness of social assistance programs in reducing poverty in Indonesia. Journal of Public Policy and Governance, 11(2), 89–102.
Hughes, S., Giest, S., & Tozer, L. (2020). Accountability and data-driven urban climate governance. Nature Climate Change, 10, 1085–1090.
Iskandar, D., & Putri, F. (2024). Infrastructure development and education accessibility in developing regions. Jurnal Pembangunan Wilayah Dan Kota, 20(1), 44–56.
Jain, A. K. (2020). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666.
Janssen, M., & Van den Hoven, J. (2024). Data-driven governance and public policy: Ethical and social implications. Springer.
Kettl, D. F. (2023). The transformation of governance: Public administration for the twenty-first century. Johns Hopkins University Press.
Kim, S., & Lee, J. (2020). Do the right thing right! Understanding the hopes and hypes of data-based policy. Government Information Quarterly, 37(3), 101491.
Kovacs-Györi, A., Ristea, A., Havas, C., Mehaffy, M., Hochmair, H. H., Resch, B., Juhasz, L., Lehner, A., Ramasubramanian, L., & Blaschke, T. (2020). Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning. ISPRS International Journal of Geo-Information, 9(12), 752.
Lambert, N., & Zanin, C. (2020). Practical handbook of thematic cartography: Principles, methods, and applications. CRC Press.
Lazer, D. M. J., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M. J., Strohmaier, M., Vespignani, A., & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062.
Lestari, W., & Kurniawan, A. (2023). Regional fiscal capacity and education quality disparity in Indonesia. Jurnal Ekonomi Pembangunan Indonesia, 24(1), 67–82.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281–297). University of California Press.
Montgomery, D. C., & Runger, G. C. (2020). Applied statistics and probability for engineers (7th ed.). Wiley.
Moore, M. H. (2024). Creating public value: Strategic management in government (Revised ed.). Harvard University Press.
Ningsih, S., Prasetyo, A., & Ramadhan, R. (2022). Public policy data integration in digital governance era. Jurnal Administrasi Publik, 12(3), 201–214.
Osborne, S. P. (2023). Public service logic: Creating value for public service users, citizens, and society. Routledge.
Pollitt, C., & Bouckaert, G. (2022). Public management reform: A comparative analysis—Into the age of austerity (4th ed.). Oxford University Press.
Pugu, M. R. (2025). Ketimpangan pendidikan dan mobilitas sosial di Indonesia [Educational inequality and social mobility in Indonesia]. Jurnal Sosiologi Pendidikan, 14(3), 210–223.
Putri, N. A. (2023). Analisis minat belajar siswa terhadap pembelajaran matematika di sekolah menengah pertama [Analysis of student learning interest in mathematics learning in junior high school]. Jurnal Pendidikan Matematika Dan Sains, 11(1), 21–30.
Rahmawati, D., & Siregar, H. (2024). Spatial inequality and public service distribution analysis. Jurnal Perencanaan Wilayah Indonesia, 9(1), 15–29.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
Safira, N., & Wibowo, Y. H. (2021). Angka partisipasi kasar dan angka partisipasi murni sebagai indikator keberhasilan pendidikan Indonesia [Gross enrollment rate and net enrollment rate as indicators of educational success in Indonesia]. Jurnal PAKAR Pendidikan, 19(2), 101–115.
Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton University Press.
Santoso, B., & Hadi, P. (2022). Urbanization and education participation gap in developing countries. Jurnal Ilmu Sosial Dan Humaniora Indonesia, 11(2), 210–224.
Saputro, H. B., & Ulkhaq, M. M. (2026). Clustering regional educational performance in Indonesia using K-Means. Journal of Computing and Smart Ecosystems.
Sari, N. N., Adzima, K. R., Sahila, S., & Khotimah, T. H. (2025). K-Means clustering to classify Indonesian provinces based on school participation and socio-economic indicators. Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam.
Schubert, E. (2022). Stop using the elbow criterion for K-Means and how to choose the number of clusters instead. arXiv.
Todaro, M. P., & Smith, S. C. (2021). Economic development (13th ed.). Pearson.
United Nations Development Programme (UNDP). (2022). Human development report 2021/2022: Uncertain times, unsettled lives: Shaping our future in a transforming world. United Nations Development Programme.
Utami, R., Wahyuni, L., & Setiawan, D. (2023). Spatial statistical approach in regional development inequality study. Jurnal Statistika Dan Aplikasi, 7(1), 1–12.
Wahyuni, S., & Setiawan, B. (2024). Machine learning implementation in public policy analytics. Jurnal Sistem Informasi Pemerintahan, 6(1), 45–58.
Wang, X., & vom Hofe, R. (2020). Selected methods of planning analysis. Springer.
Wegmann, M., Schwalb-Willmann, J., & Dech, S. (2020). Introduction to spatial data analysis. Pelagic Publishing.
World Bank. (2025). Data-driven development and public sector transformation. World Bank.
Yuliana, N., Hidayah, R., & Kurnia, D. (2023). Regional disparity and public service delivery in Indonesia. Jurnal Administrasi Dan Kebijakan Publik, 5(2), 89–104.
Yusuf, D., & Razi, F. (2025). Analisis faktor sosial ekonomi yang mempengaruhi rendahnya capaian pendidikan di Indonesia menggunakan kombinasi metode data mining. Jurnal Sistem Informasi (JUSIN).
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