An Integrated Framework for Automated Resume Screening Using RoBERTa, Random Forest and Explainable AI

Kevin Frederick Yapiter; Alfin; Yoga Hasim; Ronsen Purba; Mustika Ulina

doi:10.34148/teknika.v14i3.1359

Authors

Kevin Frederick Yapiter Department of Informatics Engineering, Faculty of Informatics, Universitas Mikroskil, Medan, North Sumatera, Indonesia
Alfin Department of Informatics Engineering, Faculty of Informatics, Universitas Mikroskil, Medan, North Sumatera, Indonesia
Yoga Hasim Department of Informatics Engineering, Faculty of Informatics, Universitas Mikroskil, Medan, North Sumatera, Indonesia
Ronsen Purba Department of Informatics Engineering, Faculty of Informatics, Universitas Mikroskil, Medan, North Sumatera, Indonesia
Mustika Ulina Department of Informatics Engineering, Faculty of Informatics, Universitas Mikroskil, Medan, North Sumatera, Indonesia

DOI:

https://doi.org/10.34148/teknika.v14i3.1359

Keywords:

Resume Screening, RoBERTa, Random Forest, Explainable AI, Recruitment

Abstract

The resume screening process is a critical stage in recruitment, yet conventional methods and traditional applicant tracking systems (ATS) often rely on manual review or keyword matching, resulting in slow, biased, and less objective evaluations. This study proposes an integrated automated screening system that combines RoBERTa for contextual feature extraction, Random Forest for candidate classification, and SHAP-based Explainable AI for interpretable decisions, enhancing transparency, efficiency, and fairness beyond traditional ATS. The dataset consists of real resumes and synthetically generated ones designed to mimic the distribution of real data, with K-means clustering used to establish labeling thresholds. Experimental results show that RoBERTa achieved an F1 Score of 81.08% in feature extraction, while Random Forest reached 96% accuracy in suitability classification. SHAP-based explanations provide insights into feature contributions for each prediction, offering an actionable understanding for recruiters. This integrated framework not only improves the efficiency and fairness of resume screening but also demonstrates a practical application of explainable AI in recruitment.

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References

[1] R. V. K. Bevara, N. R. Mannuru, S. P. Karedla, B. D. Lund, T. Xiao, H. Pasem, et al., “Resume2Vec: Transforming Applicant Tracking Systems with Intelligent Resume Embeddings for Precise Candidate Matching,” Electronics (Switzerland), vol. 14, no. 4, Feb. 2025, doi: 10.3390/electronics14040794.

[2] Z. Naveed, B. Nisar, M. Saifullah, and J. I. Baig, “Resume Ranking Using Natural Language Processing,” Journal of Computers and Intelligent Systems, vol. 2, no. 1, pp. 61-66, May. 2024.

[3] A. Rawat, S. Malik, S. Rawat, D. Kumar, and P. Kumar, “A Systematic Literature Review (SLR) On The Beginning of Resume Parsing in HR Recruitment Process & SMART Advancements in Chronological Order,” Jul. 2021, Research Square. doi: 10.21203/rs.3.rs-570370/v1.

[4] A. K. Sinha, M. A. K. Akhtar, and M. Kumar, “Automated Resume Parsing and Job Domain Prediction using Machine Learning,” Indian J Sci Technol, vol. 16, no. 26, pp. 1967–1974, Jul. 2023, doi: 10.17485/IJST/v16i26.880.

[5] Q. Trint and T. T. Dang, “Automatic Process Resume in Talent Pool by Applying Natural Language Processing,” International Conference on Logistics and Industrial Engineering, pp. 234-240, Jan. 2021.

[6] N. Novaković, L. Dražeta, “Applicant Tracking System: A Powerful Recruiters’ Tool,” In Sinteza 2024 – International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Signidunum University, pp. 240-245, 2024. doi: 10.15308/Sinteza-2024-240-245.

[7] E.Albaroudi, T.Mansouri, and A. Alameer, “A Comprehensive review of AI Techniques for Addressing Algorithmic Bias in Job Hiring,” AI, vol. 5, no. 1, pp. 383 404, Feb. 2024, doi: 10.3390/ai5010019.

[8] M. R. Sareddy, “Revolutionizing Recruitment: Integrating AI and Blockchain for Efficient Talent Acquisition,” IMPACT: International Journal of Research in Business Management (IMPACT: IJRBM), vol. 10, no. 8, pp. 33–44, Aug. 2022.

[9] Badan Pusat Statistik, “Pencari Kerja Terdaftar, Lowongan Kerja Terdaftar. Dan Penempatan/Pemenuhan Tenaga Kerja Menurut Provinsi dan Jenis Kelamin,” bps.go.id, 2023.

[10] P. Tijare, M. Waseem, M. A. Sherani, K. S. K. Sai Krishna, and P. Kavitha, “A smart resume screening tool for customized shortlisting,” ITM Web of Conferences, vol. 56, p. 04001, 2023, doi: 10.1051/itmconf/20235604001.

[11] B. Kinge, S. Mandhare, P. Chavan, and S. M. Chaware, “Resume Screening using Machine Learning and NLP: A proposed system,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 8, no. 2, pp. 253–258, Apr. 2022, doi: 10.32628/cseit228240.

[12] M. A. Aleisa, N. Beloff, and M. White, “Implementing AIRM: a new AI recruiting model for the Saudi Arabia labour market,” Journal of Innovation and Entrepreneurship, vol. 12, no. 1, pp. 59, Sep. 2023. doi: 10.1186/s13731-023-00324w.

[13] K. Gupta, R. Jinad, and Q. Liu, “Comparative analysis of NLP models for detecting depression on Twitter,” In 2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI), pp. 23-28, Jun. 2023. doi: 10.1109/CCAI59026.2023.00013.

[14] I. J. A. Biol and C. C. Abalorio, “Utilizing Named Entity Recognition for Web-Based Resume Scoring,” International Journal of Engineering Trends and Technology, vol. 72, no. 7, pp. 381-387, 2024. doi: 10.14445/22315381/IJETT-V7217P142.

[15] A. Pina, C. Petersheim, J. Cherian, J. N. Lahey, G. Alexander, and T. Hammond, “Using Machine Learning with Eye-Tracking Data to Predict if a Recruiter Will Approve a Resume,” Mach Learn Knowl Extr, vol. 5, no. 3, pp. 713–724, Sep. 2023, doi: 10.3390/make5030038.

[16] H. Al-Quhfa, A. Mothana, A. Aljbri, and J. Song, “Enhancing Talent Recruitment in Business Intelligence Systems: A Comparative Analysis of Machine Learning Models,” Analytics, vol. 3, no. 3, pp. 297–317, Jul. 2024, doi: 10.3390/analytics3030017.

[17] B. Surendiran, T. Paturu, H. V. Chirumamilla, and M. N. R. Reddy, “Resume Classification Using ML Techniques,” in Proceedings of 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023, Institute of Electrical and Electronics Engineers Inc., 2023, doi: 10.1109/IConSCEPT57958.2023.10169907.

[18] M. Aria, C. Cuccurullo, and A. Gnasso, “A comparison among interpretative proposals for Random Forests,” Machine Learning with Applications, vol. 6, p. 100094, 2021. doi: 10.1016/J.MLWA.2021.100094.

[19] G. Airlangga, “Decoding Energy Usage Predictions: An Application of XAI Techniques for Enhanced Model Interpretability,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 7, no. 2, p. 275, Apr. 2024, doi: 10.24014/ijaidm.v7i2.29041.

[20] G. Srivastava, R. H. Jhaveri, S. Bhattacharya, S. Pandya, Rajeswari, P. K. R. Maddikunta, et al., “XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions,” ACM Computing Surveys, vol. 1, no. 1, pp. 1-33, Jun. 2022, doi: 10.1145/1122445.1122456.

[21] L. Hofeditz, S. Clausen, A. Rieß, M. Mirbabaie, and S. Stieglitz, “Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring,” Electronic Markets, vol. 32, no. 4, pp. 2207–2233, Dec. 2022, doi: 10.1007/s12525-022-00600-9.