Classification of students based on quality of life and academic performance by using support vector machine / Raihana Zainordin and A.M. Farah Nabilah

Most studies done in the past on factors affecting academic performance did not touch on quality of life
factor. Also, most studies only used correlation and regression analysis. Not many studies used classification analysis. Hence, this study aimed to classify students based on quality of life and academic performance. Students’ quality of life was measured by using WHOQOL-BREF questionnaire which consists of five quality of life domains namely physical health, psychological health, social relationship, environment and overall quality of life whereas the academic performances were represented by
cumulative grade point average (CGPA). The selected sample for this study was 60 Universiti Teknologi
MARA (UiTM) Perlis students from Bachelor of Science (Hons.) Management Mathematics program. This study applied support vector machine (SVM) method for classifying the students. The results for each quality of life domain showed that students with both low and high academic performance were classified into high academic performance class. The same result was obtained when all domains were combined. All models showed high accuracy which implied that the classification made by SVM were
strongly correct. The findings of this study demonstrated that quality of life plays an important role in students’ academic performance.