Analysis of the Alignment Between Vocational High School Graduates Competencies and Industry Needs Using a Data Mining Approach

Abstract

This study aims to analyze the alignment between vocational high school graduates’ competencies and industry needs using a data mining approach. The research is motivated by the increasing gap between graduates’ skills and labor market demands in the era of rapid technological development. A quantitative method was employed, with data collected from 90 vocational high school students using a structured questionnaire. The analysis includes descriptive statistics, classical assumption tests, multiple linear regression, and data mining classification techniques to identify patterns of alignment.The results show that graduates’ competencies have a positive and significant effect on alignment level, while industry needs have a significant but negative effect, indicating that increasing industry demands may reduce alignment if competencies are not improved. Simultaneously, both variables significantly influence alignment, although the coefficient of determination (R² = 0.131) indicates a relatively low explanatory power. This suggests that other factors also contribute to alignment. The findings highlight the importance of strengthening competency-based education and enhancing collaboration between vocational schools and industry. The use of data mining provides a data-driven approach to better understand alignment patterns and support decision-making in curriculum development.