A Comparative Study of Machine Learning Algorithms for Graduation Prediction in Electrical Engineering and Informatics Department

Accurate prediction of students’ graduation time is a significant challenge for academic institutions, especially in the context of optimizing educational outcomes and resource allocation. However, there is a research gap in identifying which machine learning algorithms are best suited for this task, particularly in the Electrical and Informatics Engineering Department. This study addresses this gap by evaluating the performance of various machine learning algorithms in predicting students’ graduation time. Several algorithms, including Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), and Naive Bayes, were applied to a dataset consisting of academic and demographic records of students from a university in Indonesia. The evaluation used performance metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that LR, KNN, DT, RF, and SVM exhibited comparable accuracy rates of 74%, with a weighted average F1-score of 0.85, indicating these algorithms are effective in classifying data. In contrast, Naive Bayes, while showing superior speed with an execution time of 0.018322 seconds, achieved lower performance with an accuracy of only 39% and a weighted average F1-score of 0.44. These findings suggest that selecting an algorithm should balance the trade-off between accuracy and time efficiency. For scenarios where both are important, LR and DT are optimal choices, while Naive Bayes may be suitable for faster processing at the expense of accuracy.