Implementasi Algoritma K-Means untuk Pemetaan Tingkat Kelulusan Mahasiswa Berdasarkan Parameter Akademik Awal

Nanang
Juliandry, Alvis
Dinata, Andi Bagja

JRIIN :Jurnal Riset Informatika dan Inovasi
Vol 3 No 11 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)

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.