Naïve Bayes Classifier and Decision Tree Algorithms for Classifying Payment Data
DOI:
https://doi.org/10.30865/klik.v4i1.963Keywords:
Naïve Bayes Classifier; Decision Trees; ClassificationAbstract
In this study the authors will analyze the comparison of the naïve Bayes classifier and decision tree methods in the classification of transaction data types of payments that are often made by customers where the method will analyze which model has the best percentage. The author uses the Kaggle deals payment data set. The data mining methods used to classify data are naïve Bayes classifier, decision tree, and rule based. For this study the Naïve Bayes Classifier method will be used. The results of the research on the accuracy of data classification using a decision tree have an accuracy value of 95.60%. where the predicted data yes with yes answers totaled 232 and answer no 17 with a class precision value of 93.17%. While the predictions for no with yes answers totaled 5 and for answers no totaled 246 with a class precision value of 93.17%. Based on the results of research using the naïve Bayes classifier and decision tree, it is possible to classify data on types of deals payment based on age ranges with different accuracies. From the percentage results, the decision tree method has the highest or best percentage with a value of 95.60%, while the Naïve Bayes classifier has a value of 92.20%.
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