Analyzing the Influence of Academic Competence and Soft Skills on Vocational Students’ Work Readiness Using Regression and Machine Learning Approaches

Abstract

This study examines the influence of academic competence and soft skills on vocational high school students’ work readiness by integrating multiple linear regression and machine learning classification approaches. A quantitative method was applied using data collected from 90 final-year students. Statistical analyses included descriptive statistics, classical assumption testing, and multiple linear regression. Additionally, machine learning models Decision Tree, Naive Bayes, and Support Vector Machine (SVM) were employed to classify students’ work readiness levels. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that academic competence (p = 0.000) and soft skills (p = 0.006) significantly influence work readiness, with R² = 0.348. The SVM model achieved the highest accuracy (85.56%). These findings demonstrate that integrating statistical and machine learning approaches provides both explanatory and predictive insights.