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All Journal JOIV : International Journal on Informatics Visualization
Arshad, Mohammad Syafwan
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Computer Science & IT

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Predictive Analytics for Employability in Malaysian TVET with a Hybrid of Regression and Clustering Methods Mahdin, Hairulnizam; Nurwarsito, Heru; Baharum, Zirawani; Kamri, Khairol Anuar; Hassan, Azman; Haw, Su-Cheng; Arshad, Mohammad Syafwan
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4516

Abstract

Graduate employability remains a high concern for Technical and Vocational Education and Training (TVET) institutions, particularly within Malaysia’s Technical University Network (MTUN), where producing industry-ready graduates is a central goal. While machine learning has transformed fields like healthcare and finance, its application in vocational education remains underexplored—particularly for employability prediction. This study addresses this gap by hybridizing decision trees and clustering to uncover non-linear patterns in student survey data. Guided by Human Capital Theory and SERVQUAL, which inform variable selection (e.g., technical skills as productivity investments), this study integrates multiple linear regression, decision tree regression, and K-Means clustering to identify significant predictors and uncover latent student groupings. Using a publicly available dataset of Likert-scale responses from MTUN students, technical skills and supervisory support consistently emerged as the most impactful employability predictors. Communication showed moderate influence, while training delivery and problem-solving exhibited variable effects depending on the modelling approach. Unlike regression, decision trees revealed non-linear interaction thresholds. For example, students with SVR < 3.5 and TS < 4.0 had 40% lower employability scores, suggesting targeted mentoring could yield disproportionate improvements. Clustering revealed three distinct student profiles, which could support data-driven interventions. This hybrid framework demonstrates the potential for integrating machine learning into institutional analytics for proactive support of employability.
Co-Authors Baharum, Zirawani Hairulnizam Mahdin, Hairulnizam Hassan, Azman Heru Nurwarsito Kamri, Khairol Anuar Su-Cheng Haw