Regional Segmentation of School Dropouts Based on Economic and Accessibility Factors Using K-Means Clustering
DOI:
https://doi.org/10.52436/1.jutif.2026.7.3.5727Keywords:
Data mining, Dropout, K-Means Clustering, Regional Segmentation, Spatial AnalysisAbstract
The high dropout rate in Asahan Regency has become a serious problem affecting the quality of human resources and equitable access to education across various regions. This study aims to identify patterns and characteristics of dropout-prone areas using the K-Means clustering technique. The research method involves collecting dropout data from the Asahan Regency Education Office for the period 2022–2025, followed by data pre-processing for cleaning and normalization, and then clustering analysis to generate three regional clusters based on dropout vulnerability levels. The results indicate that clusters with high dropout rates are largely influenced by economic factors, followed by limited access to education and social conditions in the community. The resulting regional segmentation provides a spatial overview of dropout vulnerability levels in Asahan Regency. These findings offer data-driven insights that can support the formulation of more targeted education policies and programs to encourage inclusive education development in the region. Scientifically, this study contributes to strengthening the validity and effectiveness of the K-Means algorithm as a quantitative approach in mapping and identifying complex patterns in socio-educational data, thereby expanding its application in data-driven analytical studies in the field of education.
Downloads
References
U. Arfan and Y. Pekei, “An Evaluation of Teacher – Student Distribution and School Availability in Supporting Educational Development Using the K-Means Clustering Algorithm Evaluasi Distribusi Guru-Siswa dan Ketersediaan Sekolah untuk Mendukung Pembangunan Pendidikan Menggunakan K-Means Clustering,” vol. 5, no. October, pp. 1508–1516, 2025.
P. R. Alvarez-p, M. Olga, and P. A. Toledo-delgado, “Acta Psychologica Dropping out of higher education : Analysis of variables that characterise students who interrupt their studies,” vol. 252, no. December 2024, pp. 1–7, 2025, doi: 10.1016/j.actpsy.2024.104669.
S. Sukriadi and E. W. Mawarni, “Analisis Faktor Penyebab Anak Putus Sekolah : Studi Kualitatif di Dusun Lestari Setia , Kecamatan Sokan Kabupaten Melawi,” vol. 5, no. 2, pp. 1143–1152, 2025.
E. Nurkhofifah, D. Athina, A. R. Tarida, and F. A. Pratiwi, “Clustering of Junior High School Education in West Java Based on Density and Dropout Ratios Using Quartile and K- Means Methods,” pp. 483–511.
M. Vaarma and H. Li, “Technology in Society Predicting student dropouts with machine learning : An empirical study in Finnish higher education,” Technol. Soc., vol. 76, no. December 2023, p. 102474, 2024, doi: 10.1016/j.techsoc.2024.102474.
Y. Syahra et al., “Customer Segmentation Using RFM and K- Means Clustering to Support CRM in Retail Industry,” vol. 9, no. 3, pp. 1120–1131, 2025.
D. Sugiarto and R. Fitriana, “BUSINESS INTELLIGENCE IN VEGETABLE ONLINE RETAILING,” vol. 35, no. August, pp. 118–135, 2025.
A. Selina et al., “Permasalahan dalam Implementasi Pendidikan Inklusi di Sumatera Utara,” vol. 9, pp. 8876–8883, 2025.
J. T. Edward Revaldo Danuwinata1, “INFORMASI (Jurnal Informatika dan Sistem Informasi) Volume 17 No.2 / Nov / 2025,” vol. 17, no. 2, pp. 245–264, 2025.
H. Tan, “Dropout in Online Education : A Longitudinal Multilevel Analysis of Elementary Students ’ Extracurricular English Course Engagement in China,” vol. 12, 2025.
M. Psyridou, F. Prezja, M. Torppa, M. Lerkkanen, and K. Vasalampi, “Machine Learning Predicts Upper Secondary Education Dropout as Early as the End of Primary,” pp. 1–14.
D. A. Imanuel, G. Alfian, S. Vokasi, U. G. Mada, P. Korespondensi, and C. Index, “VISUALISASI SEGMENTASI PELANGGAN BERDASARKAN ATRIBUT RFM MENGGUNAKAN ALGORITMA K-MEANS UNTUK MEMAHAMI KARAKTERISTIK PELANGGAN PADA TOKO RETAIL ONLINE VISUALIZATION OF CUSTOMER SEGMENTATION BASED ON RFM ATTRIBUTES USING K-MEANS ALGORITHM TO COMPREHEND CUSTOMER CHARACTERISTICS WITHIN AN ONLINE RETAIL STORE,” vol. 12, no. 2, pp. 283–292, 2025, doi: 10.25126/jtiik.2025128619.
M. Hammam and T. Utomo, “CLUSTERING DATA SISWA PUTUS SEKOLAH DENGAN ALGORITMA K-MEANS DAN DBSCAN,” vol. 18, no. 2, pp. 150–159, 2023.
H. Safitri, S. Putri, L. Geni, F. Merry, and M. Wati, “Penerapan K-Means Clustering untuk Segmentasi Konsumen E-Commerce Berdasarkan Pola Pembelian,” vol. 7, pp. 89–99, 2025.
M. R. Hardyanto, A. O. Prameswari, and M. Rizky, “Analisis Segmentasi Pelanggan Dalam Preferensi Pembelian Produk Menggunakan Metode K-Means : Studi Kasus Industri Sepatu,” vol. 03, no. 11, pp. 1780–1792, 2024.
N. H. Ahsina, F. Fatimah, and F. Rachmawati, “BERDASARKAN PENGAMBILAN KREDIT DENGAN MENGGUNAKAN METODE K-MEANS CLUSTERING,” vol. 8, no. 3, 2022.
T. C. Saputra, S. M. Fadhilah, and S. U. Mangkuto, “Segmentation , targeting and positioning analysis using k- means clustering model : A case study of the laptop market in Indonesia,” vol. 12, no. 2, pp. 195–203, 2024.
C. H. Ardana, A. Aldita, A. Aisyah, A. Khoyum, and M. Faisal, “Segmentasi Pelanggan Penjualan Online Menggunakan Metode K- means Clustering,” vol. 9, no. 1, pp. 1–9, 2024.
S. Anwar, “Application of K-Means and C4 . 5 Algorithms for dropout risk prediction in vocational high schools,” 2024.
K. Tauhid et al., “KAJIAN UNTUK SEGMENTASI CUSTOMER BANK DENGAN ALGORITMA K-MEANS,” vol. 3, pp. 3899–3906, 2024.
D. Methods, R. Wati, B. Sembiring, F. A. Mohammed, and K. Chairuang, “Customer Segmentation Based on RFM Model Using,” vol. 11, no. 1, pp. 32–43, 2020.
N. T. Phudjashakty et al., “Segmentasi Pelanggan Grosir Menggunakan K-Means : Analisis Outlier dan Ketidakseimbangan Data,” vol. 4, no. 3, pp. 15993–16002, 2026.
E. Omol, D. Onyangor, L. Mburu, and P. Abuonji, “Application Of K-Means Clustering For Customer Segmentation In Grocery Stores In Kenya,” pp. 192–200.
J. Chitra and J. Heikal, “Customer segmentation using the K-Means Clustering algorithm in Foreign Banks in Indonesia,” vol. 11, no. 4, pp. 230–241, 2024.
E. Nurmiati and A. A. Nurindah, “Segmentasi Customer Pada Industri Ritel Menggunakan Teknik Clustering,” vol. 5, no. 1, pp. 344–353, 2025.
E. Gari and R. Sari, “Analisis Segmentasi Wilayah Penjualan menggunakan Algoritma K-Means Clustering,” vol. 7, no. 2, pp. 145–151, 2025.
A. Akram, N. Risal, D. D. Andayani, M. I. Suherman, and A. B. Kaswar, “Utilizing the K-Means Clustering Algorithm for Analyzing Student Achievement Assessment at SMK Negeri 1 Gowa,” vol. 05, no. March, pp. 60–67, 2024.
Y. Lu, S. Yeom, J. Maktoubian, and M. M. Rahman, “Improve Student Risk Prediction with Clustering Techniques : A Systematic Review in Education Data Mining,” vol. 1, pp. 1–38, 2025.
S. Lestari, “Assessment Clusterization Teacher Performance with K-Means Algorithm Clustering and Agglomerative Hierarchical Clustering ( AHC ),” vol. 9, no. 1, pp. 357–365, 2025.
P. G. Almeida, G. A. L. Silva, V. Santos, G. Moreira, P. Silva, and E. Luz, “P REDICTING A T -R ISK S TUDENTS,” no. Dl, 2024.
M. F. Fadhillah, A. Lovely, A. Suyoso, and I. Puspitasari, “Customer Segmentation with Clustering Algorithm Based on Recency , Frequency , and Monetary ( RFM ) Attributes Segmentasi Pelanggan dengan Algoritma Clustering Berdasarkan Atribut Recency , Frequency dan Monetary ( RFM ),” vol. 5, no. January, pp. 48–56, 2025.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Juna Eska, Dinda Djesmedi, Yuhandri
This work is licensed under a Creative Commons Attribution 4.0 International License.
