Efficiency Comparison of Classification Models for Pali and Sanskrit in Thai Languages Using Machine Learning Techniques
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Abstract
This research aims to test and compare the performance of classification models for Pali and Sanskrit in Thai language using Machine learning techniques. The study focuses on improving the accuracy in distinguishing between words from these two languages, which often exhibit similarities in pronunciation and spelling. Five models were tested: Random Forest, Decision Tree, K-Nearest Neighbors (K-NN), Naive Bayes, and Support Vector Machine (SVM). The evaluation process employed 10-fold cross-validation to assess model performance. The results indicate that the SVM model is the most efficient, with an accuracy of 95.75% and a precision of 90.90%. K-NN follows closely with an accuracy of 92.86%, while Naive Bayes achieves 81.29%. The Random Forest model, however, shows the lowest performance with an accuracy of only 55.27%. These findings highlight the SVM model's effectiveness in accurately classifying Pali and Sanskrit words in Thai language. The results can be applied to further developments in language classification, translation, and educational technology tools for language learning.
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