Comparative Analysis of Naïve Bayes and Decision Tree Algorithms in Data Mining Classification to Predict Weckerle Machine Productivity

Fried Sinlae (Universitas Budi Luhur)
Anugrah Sandy Yudhasti (Universitas Budi Luhur)
Arief Wibowo (Universitas Budi Luhur)

Journal of Systems Engineering and Information Technology
Vol 1 No 2 (2022): September 2022

The level of data accuracy in everyday life is necessary because it is reflected in the ever-advancing development of information technology. Analysis of data processing in information that can provide knowledge with the help of data mining systems. Algorithms commonly used for prediction are Naive Bayes and Decision Trees. The purpose of this study is to compare the Nave-Bayes algorithm and the decision tree algorithm in terms of the accuracy of predicting the productivity of the Weckerle machine at PT XYZ. The method used is a literature study from various related sources and understanding of the data in the source related to the subject of the classification method of the Naive Bayes algorithm and the decision tree into the data mining system. The results of this study are a classification using the Nave-Bayes algorithm with a higher level of confidence than the decision tree algorithm.