Implementation of The K-Nearest Neighbors (KNN) Algorithm in The Process of Student Graduation Prediction (Case Study of The Bachelor of Informatics Engineering Program, PLN Institute of Technology, Jakarta)

Andi Abd. Jalil. L (Institut Teknologi PLN)
Herman Bedi Agtriadi (Institut Teknologi PLN)
Meilia Nur Indah (Institut Teknologi PLN)
Rakhmadi Ifansyah Putra (Institut Teknologi PLN)

Jurnal E-Komtek
Vol 10 No 1 (2026)

Student graduation is one of the key indicators in a university’s Internal Quality Assurance System (SPMI). Based on data from the Bachelor of Informatics Engineering program, out of 305 students from the 2016 cohort, 227 graduated on time and 78 graduated late. This study aims to predict student graduation using the K-Nearest Neighbors (KNN) algorithm. The research stages include data collection and division for training and testing, parameter determination with K=3, and distance calculation between data points. The results show that the KNN model with parameter K=3 achieved an accuracy rate of 90% in predicting student graduation. This demonstrates that the KNN method is effective in predicting student graduation outcomes.