COMPARISON OF C4.5 AND NAIVE BAYES ALGORITHMS FOR COURSE INTEREST AT STATE VOCATIONAL HIGH SCHOOL 6 BATANGHARI
DOI:
https://doi.org/10.33998/jakakom.2025.5.2.2287Keywords:
Data Mining, Data Mining, Classification, Selling, Naïve Bayes., RapidMinerAbstract
This study aims to analyze the comparison of the C4.5 and Naive Bayes algorithms in the selection of majors at SMK Negeri 6 Batanghari. The comparison between the C4.5 and Naive Bayes algorithms is carried out because of the effectiveness and accuracy in decision making. The C4.5 method as a decision tree algorithm provides a clear and easy-to-understand interpretation of the decisions taken, while Naive Bayes with its probabilistic approach is often faster and more efficient in handling large datasets. From the results of the analysis carried out, this study revealed that the two algorithms have different characteristics and performance in classifying student interest data. The C4.5 algorithm based on decision trees tends to be easier to interpret because it produces clear decision rules, while Naive Bayes based on probability has advantages in handling data uncertainty
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