International Journal For Multidisciplinary Research

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Predicting the Employability of Filipino Students Using Mock Job Interview Data: A Comparative WEKA Classifier Analysis

Author(s) Mr. Alven Paymalan Pardo, Mosa, Jhedelson A., Barol, Rhebie D., Catuburan,Shane Ray B
Country Philippines
Abstract The research investigates how data mining methods can predict whether Filipino students will secure jobs through testing these methods with the WEKA machine learning platform. The research evaluated four algorithms which included Naïve Bayes, SMO, IBk, and Logistic Regression through 10-fold cross-validation using 2,982 mock job interview records obtained from university agencies throughout the Philippines. The results show that SMO is the highest-performing classifier, achieving 92.54% accuracy, followed by IBk at 89.97%, Logistic Regression at 88.45%, and Naïve Bayes at 85.21%. The most important soft-skill attributes which interviewers based their assessments on included Mental Alertness and Communication Skills and Self-Confidence and Ability to Present Ideas, but the dataset showed Communication Skills and Mental Alertness as the most effective attributes for class separation. SMO showed the best performance through its predictive capabilities, but the system lacked explainability, whereas IBk established dependable performance, and Logistic Regression delivered an easily understandable model with similar accuracy results, while Naïve Bayes operated as the weakest basic model due to its easy-to-use nature. Davao Region higher education institutions should use SMO-based predictive modeling along with employability scoring and targeted soft-skills interventions to identify at-risk students through their early identification process which will enhance mock interview training and career guidance and pre-employment preparation.
Keywords Filipino Students, Employability Prediction, WEKA, Data Mining, Machine Learning, Mock Job Interview
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-09
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.77527
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