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 (Lembaga Penelitian dan Pengabdian Masyarakat, 2026)
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.