Implementasi Analisis Data Akademik Menggunakan Teori Himpunan dan Algoritma Clustering - Farid Deza Amin Nur Rasyid; Arlin Fajar Saragih; Muslim Gunawan; Syahrandy Fazra; Maryana (LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL), 2026)
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.