A Design and Development of Food Security Management System for Household-level FCS Prediction using Machine Learning
DOI:
https://doi.org/10.14456/jiskku.2024.30Keywords:
Algorithms, Food security, Machine learningAbstract
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.
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