<![CDATA[Classification is the process of identifying and grouping an object into the same group or category Classification can be used to group a large-sized dataset, and some commonly used classification methods are CART (Classification And Regression Tree) and Naïve Bayes. This study discusses the comparison of CART and Naïve Bayes methods by measuring accuracy, precision, recall, and f1-score values with 3 scenarios of training and testing dataset distribution. Accuracy, precision, recall, and f1-score measurements are performed using a confusion matrix. The scenarios for training and testing dataset division are 70%, 80%, and 90% of the training dataset. From the results of the study, CART has the highest average accuracy and f1-score of 79.616% and 57.636% respectively, while the highest average accuracy and f1-score of Naïve Bayes are 75.104% and 62.004% respectively.]]>
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