Machine Learning Algorithms with Parameter Tuning to Predict Students’ Graduation-on-time: A Case Study in Higher Education

Rizal Bakri ((1) Statistics Research Group, STIEM Bongaya, Jl. Mappaoudang No. 28, Makassar, 90223, Indonesia (2) Department of Digital Business, Universitas Negeri Makassar, Jl. Pendidikan No. 1, Makassar, 90222, Indonesia)
Niken Probondani Astuti (Department of Management, STIEM Bongaya, Jl. Mappaoudang No. 28, Makassar, 90223, Indonesia)
Ansari Saleh Ahmar (Department of Statistics, Universitas Negeri Makassar, 90223, Makassar, Indonesia)

Journal of Applied Science, Engineering, Technology, and Education
Vol. 4 No. 2 (2022)

This study aims to predict a student’s graduation on time (GOT) using machine learning algorithms. We applied five different machine learning algorithms, namely Random Forest, Support Vector Machine (Linear Kernel), Support Vector Machine (Polynomial Kernel), K-Nearest Neighbors, and Naïve Bayes. These algorithms were tested using 10-fold cross validation and simulated various parameter tuning values. The results show that the Random Forest algorithm produces the best accuracy and kappa statistics values, so this algorithm is suitable for modeling predictive data of students graduating on time. This predictive model is expected to be useful for higher education management in designing their strategies to assist and improve student academic performance weaknesses in order to achieve graduation on time.