Analisis Pemerataan Pendidikan di Indonesia Menggunakan Reduksi Dimensi PCA dan Klasterisasi K-Means

Authors

  • Erwin Arry Kusuma Teknik Informatika, STMIK Banjarbaru
  • Adani Dharmawati Teknik Informatika, Fakultas Teknologi Informasi, Universitas Islam Kalimantan MAB

DOI:

https://doi.org/10.37859/jf.v16i1.11349
Keywords: educational equity, k-means clustering, principal component analysis

Abstract

Educational equity in Indonesia continues to face substantial challenges due to significant disparities in achievement across provinces. This study aims to map these gaps by combining Principal Component Analysis (PCA) for dimensionality reduction and K-Means Clustering for regional grouping. Utilizing 2023 data from the Indonesian Central Bureau of Statistics (BPS) with eight key indicators, the analysis reveals that three principal components effectively capture 91.85% of the data variance. The clustering procedure successfully categorizes provinces into two distinct groups: 36 provinces in the high-achievement cluster and two provinces that lag significantly (Central Papua and Papua Mountains). A Silhouette Score of 0.782 confirms the high validity and consistency of the clustering results. These findings serve as a critical alert for policymakers to implement targeted interventions in underperforming regions to prevent further widening of the educational gap.

Downloads

Download data is not yet available.

References

R. Syahputra, "Analisis Ketimpangan Pendidikan antar Wilayah di Indonesia.," Jurnal Ekonomi dan Kebijakan Publik, vol. 12, no. 1, pp. 45-58, 2021.

A. Nurahman, "Disparitas Kualitas Sumber Daya Manusia di Wilayah Tertinggal," Jurnal Sosial dan Humaniora, vol. 8, no. 2, pp. 112-125, 2022.

Badan Pusat Statistik, Potret Pendidikan Indonesia: Statistik Pendidikan 2023, Jakarta: BPS RI, 2023.

K. d. F. M. Anwar, "Penerapan Principal Component Analysis untuk Reduksi Dimensi pada Data Multivariat," Jurnal Sains Data, vol. 4, no. 1, pp. 12-20, 2020.

A. Sudrajat, "Evaluasi Kebijakan Pemerintah dalam Pemerataan Pendidikan Nasional," Jurnal Administrasi Publik, vol. 11, no. 2, pp. 101-115, 2020.

J. K. M. d. P. J. Han, Data Mining: Concepts and Techniques. 3rd ed, Waltham: Morgan Kaufmann, 2012.

M. A. Syakur, "Integrasi Algoritma PCA dan K-Means dalam Analisis Klasterisasi," Jurnal Teknologi Informasi, vol. 15, no. 3, pp. 201-210, 2021.

S. Raschka, Python Machine Learning, Birmingham: Packt Publishing, 2015.

I. T. d. C. J. Jolliffe, "Principal Component Analysis: A Review and Recent Developments," Philosophical Transactions of the Royal Society A, vol. 374, no. 2061, 2016.

J. Shlens, "A Tutorial on Principal Component Analysis," arXiv preprint arXiv:1404.1100, 2014.

S. Sauri, "Analisis Multivariat dalam Pemetaan Pendidikan Dasar," Jurnal Pendidikan Dasar, vol. 3, no. 2, pp. 15-28, 2019.

A. Suryadi, "Metodologi Penelitian Pendidikan dan Validitas Data Sekunder," Jurnal Ilmiah Pendidikan, vol. 7, no. 1, pp. 34-46, 2020.

S. Hardiani, "Pentingnya Standarisasi Data pada Algoritma Berbasis Jarak," Jurnal Informatika, vol. 10, no. 2, pp. 88-97, 2021.

L. d. R. P. J. Kaufman, "Finding Groups in Data: An Introduction to Cluster Analysis.," New York, John Wiley & Sons, 2009.

I. T. d. C. J. olliffe, "Principal Component Analysis: A Review and Recent Developments.," hilosophical Transactions of the Royal Society A, vol. 374, no. 2061, 2016.

H. d. W. L. J. Abdi, "Principal Component Analysis," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, no. 4, pp. 433-459, 2010.

J. F. B. W. C. B. B. J. d. A. R. E. Hair, Multivariate Data Analysis. 7th ed, Harlow: Pearson Education, 2014.

P. d. K. A. Bholowalia, "EBK-Means: A Clustering Technique based on Elbow Method and K-Means," International Journal of Computer Applications, vol. 105, no. 9, pp. 17-24, 2014.

P. J. Rousseeuw, "Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis," Journal of Computational and Applied Mathematics, vol. 20, pp. 53-65, 1987.

D. L. d. B. D. W. Davies, "A Cluster Separation Measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 224-227, 1979.

Downloads

Published

2026-04-30

Issue

Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)

Section

Articles

License

Copyright (c) 2026 Erwin Arry Kusuma, Adani Dharmawati

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright Notice

An author who publishes in the Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) agrees to the following terms:

  • Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-ShareAlike 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
  • Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
  • Author is  permitted and encouraged to post his/her  work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).

Read more about the Creative Commons Attribution-ShareAlike 4.0 Licence here: https://creativecommons.org/licenses/by-sa/4.0/.