Implementasi Algoritma K-Means untuk Pemetaan Tingkat Kelulusan Mahasiswa Berdasarkan Parameter Akademik Awal - Nanang; Alvis Juliandry; Andi Bagja Dinata (Jurnal Riset Informatika dan Inovasi, 2026)
This study aims to classify students based on their academic performance during the first four semesters in order to map their potential for on-time graduation. Using the K-Means algorithm, the data were processed into three clusters: High, Medium, and Low. The results indicate that second-semester GPA and the number of credits taken are the dominant factors influencing the clustering. The model achieves a clustering accuracy of 71%, which can be utilized by academic programs as an early warning system.