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A Design and Development of Food Security Management System for Household-level FCS Prediction using Machine Learning |
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| รหัสดีโอไอ | |
| Creator | Thongchai Chuachan |
| Title | A Design and Development of Food Security Management System for Household-level FCS Prediction using Machine Learning |
| Contributor | Kritsananut Nunchoo, Suwat Gluaythong, Pajaree Prasertpol |
| Publisher | Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen University |
| Publication Year | 2567 |
| Journal Title | Journal of Information Science Research and Practice |
| Journal Vol. | 42 |
| Journal No. | 4 |
| Page no. | 88–101 |
| Keyword | Algorithms, Food security, Machine learning |
| URL Website | https://www.tci-thaijo.org/index.php/jiskku/index |
| Website title | Journal of Information Science Research and Practice |
| ISSN | 3027-6586 |
| Abstract | Purpose: This study aims to design and develop a data collection and analysis system capable of accurately predicting the Food Consumption Score (FCS) within the context of Thailand, supporting effective food security management.Methodology: A data collection and analysis system was developed and utilized by a network of food security researchers from 10 Rajabhat Universities. The collected data were applied to machine learning algorithms, including Naïve Bayes, Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost), to build an FCS prediction model.Findings: The study found that the XGBoost algorithm demonstrated the highest accuracy in predicting FCS, with a precision rate of at least 99%. This highlights its potential as a predictive tool for food security in Thailand.Application of this study: The developed system and model can serve as critical tools for monitoring and forecasting household-level food security. They can support decision-making, policy planning, and the efficient management of food security challenges with speed and precision. |
