คอลเล็กชัน (Collections)
พบ 2 รายการ
(ค้นหา: "machine learning model")
วารสารวิชาการ
ระดับนานาชาติ (SCOPUS) Q2
Digital Workforce Matching: A Machine Learning Approach for Skill-Based Job Classification and Recom
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.
บทความประชุมวิชาการ
ระดับนานาชาติ
A Comparative Study of Machine Learning Models for Forecasting the Stock Exchange of Thailand (SET)
Accurate stock forecasting in the Thai capital market remains challenging due to high volatility and pronounced non -linear dynamics in financial time-series. This study conducts a comparative evaluation of machine learning (ML) models for one-day-ahead prediction of daily closing prices of stocks listed on the Stock Exchange of Thailand (SET). Two widely used technical indicators-Simple Moving Average (SMA) and Exponential Moving Average (EMA)-are employed as input features to capture trend and momentum. Five years of daily data (2019–2023) for three large-cap stocks (PTT. BK, BBL.BK, and CP ALL.BK) are used to assess model robustness under multiple market conditions. We examine three train-test splits (70:30, 80:20, 90: 10) to evaluate stability across data regimes. Experiments show that Linear Regression consistently outperforms competing approaches, achieving the highest coefficient of determination (R2) of 0.9758, and producing one-day-ahead forecasts closely aligned with actual closes for PTT.BK, BBL.BK, and CPALL.BK. The findings indicate that a systematic pipeline grounded in carefully selected technical indicators can provide an effective framework for stock-trend analysis and support investment decision-making in the volatile Thai equity market.