In the era of Industry Revolution (IR) 4.0, business and industry are being transformed by a new wave of digital technology. In order to boost the economy’s prosperity in Malaysia, skilled workforce or well-trained manpower is vital in accomplishing the goal.However, it requires mainstreaming Technical and Vocational Education Training (TVET)in education system by providingcomprehensive training, effective research consultancy, holistic courses, collaboration, student placement and program attachment opportunity. Coherent from this issue, the government can produce more skill workers that can handle the rapid changing world of work. In Malaysia, there are more than 1000 TVET institutions, where 506 are considered as public institutions. However, itstill receives less attention by the students after secondary education. The identified potential factors are TVET instructors, current policy in Malaysia, social perception, employers’ perception, parents, facility, education cost and student themselves. Therefore, this study aims to rank these factors according to the levels of importance using Analytic Hierarchy Process (AHP) method. AHP is a method used to rank criteria by assigning the weight for each criterion. In this study, primary data is collected using questionnaires from 32 TVET instructors of Institut Kemahiran Belia Negara (IKBN) in northern region of Malaysia. The result of AHP shows that the variable of parents is the most influential factor with the weight of 18.81%, followed by the variable of facilities (18.56%). Conversely, the least influential factor is the variable of social perception with the weight of 7.21%. Hence, the government should implement appropriate strategies to attract more secondary school students to enroll in TVET programs. Due to the growth of skilled workers, our country is expected to transform the landscape of the manufacturing industry over the next decade. Hence, developingthe country’s productivity and curbing youth unemployment.

The wave of Industry Revolution (IR 4.0) highlights the importance of technology in our life. The demand for technologist and skilled workers in Technical and Vocational Education and Training (TVET) are increasing day by day due to their expertise. TVET provides a platform for formal and non-formal learning to equip the youngsters in contributing to the development of a prosperous and inclusive nation. Moreover, TVET promises bright job prospects especially in fulfilling the manpower demand of IR 4.0.  However, students in Malaysia currently are not fully aware of the existence of TVET, since the number of students who join TVET are still below expectation. Therefore, the main objective in this study is to develop the best TVET model to classify the students’ tendency in choosing TVET after completing secondary school. From the literature, five main factors that hinder students’ interest in joining TVET are recognized, namely students’ interest, parents, society, TVET instructors and employers. In this study, 428 secondary school students from Kedah (Malaysia) are involved as respondents. Different types of decision tree models are developed based on the algorithms and the splitting criteria. Altogether, there are 15 variables derived from 5 main affecting factors mentioned above to determine the tendency of joining TVET. Consequently, the best TVET classifier with the misclassification rate of 0.2919 is selected, to predict the tendency of students who will be joining TVET in future. Our findings revealed that the variable of “Stream” plays as the primary and trifling roles. This classifier is beneficial in assisting the government to achieve the aim of upholding TVET in Malaysia.