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

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.