Predicting Students’ Inclination to TVET Enrolment Using Various Classifiers

Technical and Vocational Education Training (TVET) is an education system that delivers necessary information, skills, attitudes related to work or self-employment. However, the TVET program not preferred by most Malaysian students due several factors such as students’ interest, parental influence, employers’ negative impression, facility in vocational institutions, inexperienced instructors, society’s perception. Consequently, it raises issue of skilled workers shortage. The gravest threat will be far-reaching, pushing our economy into depreciation. Therefore, important identify traits interests before conducting further investigation turn thrive this phenomenon. This study aims utilise classifiers (Decision Tree, Neural Network, Logistic Regression Naïve Bayes) predict inclination join programmes. A total 428 secondary school from Kedah, Malaysia, are chosen survey respondents. best classifier determined according lowest misclassification rate. findings revealed Decision Tree-based Gini Index with three branches prevail against other a rate 0.1938. could act steer for Kedah Department (DOE), parties, agency implementing effective strategies enliven inspire programs.