Predicting University Graduates Employability Using Support Vector Machine Classification

The absorption of graduates into the world of work is a key indicator of higher education institution success, especially amid the tight job market competition due to increasing graduate numbers. Understanding employability and the factors that influence it is crucial for higher education institution to enhance education quality and facilitate graduates' transitions to employment. This research aimed to predict the employability of Telkom University students through their initial job income. Methods involved feature manipulation techniques like Principal Component Analysis, Spearman's rank correlation, and the Chi-square test of independence, followed by SMOTE-ENN to address data imbalance. Modeling was conducted using a Support Vector Machine with Randomized Search hyperparameter tuning, analyzed through Permutation Feature Importance to identify factors affecting employability. The result showed the enhanced SVM model with SMOTE-ENN, Spearman’s rank correlation coefficient as feature selection and randomized search hyperparameter tuning achieved the highest precision, recall, f-score, and accuracy of approximately 0.70, 0.73, 0.71, and 0.73, respectively. Competency features such as ethics, english skills, IT skills, and knowledge were identified as the most influential factors.