The Creation of Prediction Model for the Volumes of Thailand's Orchid Exports by Applying Machine Learning Methodologies - Tantrabundit, Pongsatorn; Phothong, Lersak; Sukprasert, Anupong (Journal of Business, Innovation and Sustainability (JBIS), 2024)
This study proposes a machine learning-based model for forecasting Thai orchid exports, aiming to aid decision-making processes, and ensuring high-quality products to meet global demand. The research employed the regression model with three estimation methods (1) K-Nearest Neighbors, (2) Random Forest, and (3) Deep Learning. Data from six websites spanning January 2011 to July 2023 included export volumes, prices, exchange rates, labor wages, and various economic indicators. The analysis involved data understanding, preparation, and modeling, with evaluation metrics such as Mean Squared Error (MES), Root Mean Squared Error (RMES), and Absolute Error (AE). Evaluation results reveal that the Random Forest technique exhibited the least error among the three methods, emphasizing that it was suitable for constructing a predictive model for orchid exports.