Phoemchalard C.Senarath N.Malila P.Tathong T.Khamhan S.Mahidol University2025-02-112025-02-112024-11-01International Journal of Agricultural Technology Vol.20 No.6 (2024) , 2497-2512https://repository.li.mahidol.ac.th/handle/20.500.14594/104242Orange Data Mining study on the classification of buffalo, beef, and goat meats, Machine Learning (ML) classifiers including Support Vector Machine (SVM), Neural Network (NN), and Naïve Bayes (NB) are well performed to achieve 100% accuracy across all features. Random Forest (RF) demonstrated the best performance more than 97% in AUC, CA, F1, and MCC. Other models such as Gradient Boosting (GB), AdaBoost, CN2 Rule Induction (CN2), Decision Tree (DT), and k-Nearest Neighbors (KNN) are performed better but there were less efficient. In the application of specific classifiers for species-based meat quality attributes, SVM, NN and NB should be considered as the best options.Agricultural and Biological SciencesArticleSCOPUS2-s2.0-8521628743726300192