Analisis Pengelompokan Pola Pembayaran UKT Mahasiswa Menggunakan Algoritma K-Means Clustering: Penelitian

Desmulyati Desmulyati; Imam Budiawan; Feri Andrianto; Reafael Andrian Canavaro; Muhammad Haikal Nugroho; Sofiyan Aris Saputra

doi:10.31004/jerkin.v4i3.4967

Authors

Desmulyati Desmulyati Universitas Bina Sarana Informatika
Imam Budiawan Universitas Bina Sarana Informatika
Feri Andrianto Universitas Bina Sarana Informatika
Reafael Andrian Canavaro Universitas Bina Sarana Informatika
Muhammad Haikal Nugroho Universitas Bina Sarana Informatika
Sofiyan Aris Saputra Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31004/jerkin.v4i3.4967

Keywords:

UKT, K-Means Clustering, Payment Pattern, Machine Learning, Higher Education

Abstract

Single Tuition Fee (UKT) plays a crucial role in financing higher education, but late and arrear payments are often difficult to analyze manually. This study aims to classify student UKT payment patterns using the K-Means algorithm based on per capita income, UKT amount, lateness, lateness category, and total arrears. The data used were 300 cleaned and standardized students. The number of clusters was determined using the Elbow and Silhouette Score methods, with the best results at k = 3 (SSE = 524.06; Silhouette Score = 0.5609). The three clusters include high-income students with regular payments, low-income students with minor delays, and high-risk students with large delays and arrears. These results help universities map UKT payment risks and develop more targeted collection and relief policies.

References

Yustanti, W., Nurhidayat, A. I. & Java, M. I., 2025. Optimizing Tuition Fee Determination with K-Means Cluster Relabeling Based on Centroid Mapping of Principal Component Pattern. Journal of Information Systems Engineering and Business Intelligence, 11(3), pp. 445–458.

Kurniawan, H., Defit, S. & Sumijan, 2020. Data Mining Menggunakan Metode K-Means Clustering Untuk Menentukan Besaran Uang Kuliah Tunggal. Journal of Applied Computer Science and Technology (JACOST), 1(2), pp. 80–89.

Sophia, A. & Jasmir, J., 2024. Penerapan Data Mining dalam Pengelompokan Uang Kuliah Tunggal (UKT) Menggunakan Metode K-Means pada Universitas Jambi. Jurnal Manajemen Sistem Informasi, 9(1), pp. 24–38.

Fahriya, K. & Yustanti, W., 2021. Optimalisasi Jumlah Klaster Uang Kuliah Tunggal pada Data Sosial Ekonomi Mahasiswa. JEISBI (Journal of Emerging Information Systems and Business Intelligence), 2(2), pp. 73–77.

Rosdianti, E. & Sugianto, C. A., 2023. Analisis Keterlambatan Pembayaran Sumbangan Pembinaan Pendidikan (SPP) dengan Menggunakan Algoritma K-Means (Studi Kasus Politeknik TEDC Bandung). Journal Informatics and Electronics Engineering, 3(1), pp. 25–32.

Talib, N. I. M., Abd Majid, N. A. & Sahran, S., 2023. Identification of Student Behavioral Patterns in Higher Education Using K-Means Clustering and Support Vector Machine. Applied Sciences, 13(5), art. 3267.

Goh, Y. L., Goh, Y. H., Yip, C.-C., Ting, C. H., Bin, R. L. L. & Chen, K. P., 2020. Prediction of Students’ Academic Performance by K-Means Clustering. Science Proceedings Series, 2(1), pp. 1–6.

Liu, R., 2022. Data Analysis of Educational Evaluation Using K-Means Clustering Method. Computational Intelligence and Neuroscience, 2022, Article ID 3762431, 10 pages.

Syahputra, I., Fonna, N., Fitriani, R. & Tahlil, T., 2025. Application of K-Means Algorithm to Cluster Students’ Reading Patterns in the Digital Age. Jurnal Inovtek Polbeng Seri Informatika, 10(1), pp. 320–331.

Siregar, H. L., Hidayanthi, R. & Sakti, A. L. D., 2024. Implementation of K-Means Clustering on Student Learning Achievements Based on Social Economic and Social Related. Research and Development in Education (RaDEn), 4(2), pp. 1447–1459.