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Submitted
21 January 2026
Accepted
22 May 2026
Published
31 May 2026
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Abstract

The education-job mismatch phenomenon remains a significant challenge for university graduates, where many individuals work in fields that are not aligned with their educational background and competencies. Career decision-making processes are also generally subjective and have not fully leveraged data-driven analysis. This study aims to design and implement a post-graduation career-fit prediction system based on resume screening using a machine learning approach. The proposed method employs two supervised classification algorithms, namely Random Forest and Support Vector Machine (SVM), with feature representation using TF-IDF based on n-grams on a dataset of 13,389 resumes. The results indicate that both models achieve strong performance; however, SVM outperforms Random Forest, achieving an accuracy of 84.39% and an F1-score of 83.36%, compared to 81.96% accuracy for Random Forest. Feature importance analysis reveals that technical skills, work experience, and field of study are the most influential factors in determining career fit. This study contributes a data-driven predictive approach to support more objective career decision-making for students and graduates.

Keywords

machine learning random forest resume screening support vector machine

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Copyright (c) 2026 Jurnal Komputer Terapan

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Author Biography

Yutika Amelia Effendi, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga

Faculty of Advanced Technology and MultidisciplineUniversitas Airlangga
How to Cite
Effendi, Y. A., Nadhir, A., Ebenezer Situngkir, H. H. A., Aradea, R. K., & Setiono, S. C. (2026). SISTEM PREDIKSI KECOCOKAN KARIR PASCA STUDI BERBASIS RESUME SCREENING MENGGUNAKAN METODE RANDOM FOREST DAN SVM. Jurnal Komputer Terapan, 12(1), 1–11. https://doi.org/10.35143/jkt.v12i1.6883
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