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Digital Workforce Matching: A Machine Learning Approach for Skill-Based Job Classification and Recommendation

ผศ.ดร.ณัฐรฐนนท์ กานต์รวีกุลธนา | digital workforce matching skill-based job classification

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Digital Workforce Matching: A Machine Learning Approach for Skill-Based Job Classification and Recommendation is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Thailand License.

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Collection	RMUTK Research Repository (RMUTK IR) วารสารวิชาการ — e-Journal Articles
ID RMUTK Digital	RMUTK000072
Title	Digital Workforce Matching: A Machine Learning Approach for Skill-Based Job Classification and Recommendation
Alternative title	Digital Workforce Matching: A Machine Learning Approach for Skill-Based Job Classification and Recommendation
Authors	ผศ.ดร.ณัฐรฐนนท์ กานต์รวีกุลธนา Corresponding
Faculty	คณะบริหารธุรกิจ
Journal Title	Journal of Current Science and Technology
ISSN	2630-0656
Volume / Issue / Pages	Vol.15 \| No.4 \| pp.1-17
Published	2025-09-20
Year	2568
Level	ระดับนานาชาติ (SCOPUS)
Quartile	Q2
DOI	https://doi.org/10.59796/jcst.V15N4.2025.137
Abstract	This research presents an integrated machine learning approach for optimizing digital workforce matching in Thailand's evolving digital economy. The study develops a novel job recommendation system combining Natural Language Processing (NLP) with Random Forest classification to analyze job market data from Thailand's leading recruitment platforms. Using FastText for initial job classification and a Random Forest model for skill-based matching, the system achieves 75% accuracy in job recommendations across 20 digital job categories. The methodology incorporates automated skill extraction, cross-validated model comparison, and a user-friendly web interface for practical applications. Our findings reveal distinct skill clusters and job-skill relationships in Thailand's digital sector, with the Random Forest model outperforming traditional Decision Tree approaches by 4% in accuracy metrics. The system demonstrates robust performance in real-world testing, achieving 86.67% accuracy in matching previously unseen job postings. This research contributes to both theoretical understanding of skill-based job matching and practical workforce development, offering insights for curriculum development and career planning for workforce development stakeholders in Thailand's digital sector.
Abstract (EN)	This research presents an integrated machine learning approach for optimizing digital workforce matching in Thailand's evolving digital economy. The study develops a novel job recommendation system combining Natural Language Processing (NLP) with Random Forest classification to analyze job market data from Thailand's leading recruitment platforms. Using FastText for initial job classification and a Random Forest model for skill-based matching, the system achieves 75% accuracy in job recommendations across 20 digital job categories. The methodology incorporates automated skill extraction, cross-validated model comparison, and a user-friendly web interface for practical applications. Our findings reveal distinct skill clusters and job-skill relationships in Thailand's digital sector, with the Random Forest model outperforming traditional Decision Tree approaches by 4% in accuracy metrics. The system demonstrates robust performance in real-world testing, achieving 86.67% accuracy in matching previously unseen job postings. This research contributes to both theoretical understanding of skill-based job matching and practical workforce development, offering insights for curriculum development and career planning for workforce development stakeholders in Thailand's digital sector.
Keywords (TH)	digital workforce matching skill-based job classification recommendation machine learning model
Keywords (EN)	digital workforce matching skill-based job classification recommendation machine learning model
Access Level	Open Access (เปิดสาธารณะ)

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https://doi.org/10.59796/jcst.V15N4.2025.137

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