Implementasi Analisis Data Akademik Menggunakan Teori Himpunan dan Algoritma Clustering
DOI:
https://doi.org/10.62671/jikum.v2i2.315Keywords:
Set Theory, Discrete Mathematics, Academic Data, KIP-Kuliah, SNBT UNIMALAbstract
Processing academic data in large-scale higher education institutions requires a structural approach to facilitate analysis and decision-making. This study examines the application of Set Theory in discrete mathematics for grouping academic data of new students at Universitas Malikussaleh (UNIMAL) who passed the 2026 National Selection Based on Test (SNBT) and their eligibility status for the Kartu Indonesia Pintar Kuliah (KIP-K). The research method was conducted by representing student data as a universal set and grouping them based on faculty attributes, scientific fields, and KIP-K status. The analysis results show that Set Theory successfully models the academic data structure formally through the concepts of partition, intersection, and set difference. Out of a total of 2,917 students who passed the SNBT, 899 students (30.82%) were declared eligible for KIP-K. The Faculty of Teacher Training and Education (FKIP) recorded the highest ratio of KIP-K recipients (45.32%), while the Faculty of Medicine had the lowest ratio (13.88%). This modeling proves that set theory acts not only as a theoretical concept in discrete mathematics but also as a practical analytical instrument in academic information systems.
References
Aprudi, S., & Etriyanti, E. (2026). Penerapan Metode K-Means Clustering untuk Pengelompokan Peminatan Unit Kegiatan Mahasiswa. Jurnal Pustaka Data, 6(2). https://doi.org/10.55382/jurnalpustakadata.v6i2.1864 (doi.org in Bing)
Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques. Morgan Kaufmann.
Manuhutu, J. V., Makaruku, Y. N., & Halauwet, D. (2026). Analisis dan Visualisasi Tingkat Kelulusan Mahasiswa Menggunakan Algoritma K-Means Clustering dan Silhouette Score Berdasarkan Asal Daerah. Progresif: Jurnal Ilmiah Komputer, 22(2). https://doi.org/10.35889/progresif.v22i2.3663 (doi.org in Bing)
Nurhaswinda, N., Azzahra, G., Qonitah, M., & Azzahra, N. (2025). Pemahaman Dasar Himpunan dalam Matematika: Konsep, Operasi dan Penerapannya di Dunia Nyata. Cendekia Pendidikan, 4(1). https://doi.org/10.36841/cendekiapendidikan.v4i1.6137 (doi.org in Bing)
Susanto, H., Rahmat, Y. S., & Saadah, R. (2025). Peranan Himpunan sebagai Fondasi Matematika Diskrit dan Implikasinya terhadap Pengembangan Sistem Komputasi. Journal of Computer Science and Information Technology, 3(1). https://doi.org/10.70248/jcsit.v3i1.3465 (doi.org in Bing)
Viola, A., & Syabila, Y. (2025). Analisis Efektivitas Program KIP Kuliah dalam Meningkatkan Prestasi Akademik Mahasiswa. Institut Syekh Abdul Halim Hasan Binjai.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
Kaufman, L., & Rousseeuw, P. J. (2009). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Munir, R. (2012). Matematika Diskrit (Revisi Kelima). Bandung: Informatika Bandung.
Rosen, K. H. (2019). Discrete Mathematics and Its Applications (8th ed.). McGraw-Hill Education.
