Educational data mining model using support vector machine for student academic performance evaluation

In the educational landscape, educational data mining has emerged as an indispensable tool for institutions seeking to deliver exceptional and high-quality education. However, education data revealed suboptimal academic performance among a significant portion of the student population, which consequently resulted in delayed graduation. This experimental research generally aims to evaluate student graduation outcomes. Meanwhile, the specific aim is to predict student academic performance by applying the support vector machine (SVM) model based on sampling techniques. The proposed model is evaluated using datasets originating from one of the State Islamic Universities. The dataset has both on-time and delayed graduation status. The results show that the support vector machine model based on the shuffle sampling on the Arabic language and literature (BSA) dataset produces excellent performance on both tests with accuracy values above 90% and area under the curve (AUC) above 0.9. Meanwhile, the Islamic education management (MPI) dataset produces excellent performance when applying a support vector machine based on stratified sampling with accuracy values above 90% and AUC above 0.9. Therefore, it could be concluded that the proposed model has excellent and reliable performance.