Modeling Student’s Academic Performance During Covid-19 Based on Classification in Support Vector Machine

This study proposed a statistical investigate the pattern of students’ academic performance before and after online learning due to Movement Control Order (MCO) during pandemic outbreak modelling based on classification in Support Vector Machine (SVM). Data sample were taken from undergraduate students Faculty Science Mathematics, Universiti Pendidikan Sultan Idris (UPSI). Student’s Grade Point Average (GPA) obtained developed model performances Covid-19 outbreak. The prediction was used predict university when classes conducted. algorithm (SVM) develop university. For algorithm, there are two important parameters which C (misclassification tolerance parameter) epsilon need identify proceed further analysis. applied four different types kernel is linear kernel, radial basis function polynomial sigmoid result found that best accuracy achieved by SVM 73.68% using worst 67.99% with parameter misclassification 128 0.6.