Comparison Of Naïve Bayes And Decision Trees In Determining The Best Manager Of Nurul Jadid Islamic Boarding School Based On Forward Selection

Abstract

In an effort to find a solution for determining the best administrators, Islamic boarding school administrators try to determine the nominations for the best administrators using existing service data and knowledge. The process of determining nominations for the best administrators is less accurate, requiring computational methods to classify which administrators fall into the best category. In the context of data mining, classification is an important aspect. One of the classification models used is Naïve Bayes which focuses on class probability, and Decision Tree C4.5 which produces a decision tree to determine the priority of indicators that are most influential in predicting the best management. Both of these algorithms have their respective advantages. This research aims to analyze and compare the performance of the Naïve Bayes and Decision Tree classification algorithms. The comparative results of testing the Naïve Bayes and C4.5 algorithms in determining the nominations for the best administrators at the Nurul Jadid Paiton Probolinggo Islamic Boarding School on 455 administrator data tested in this study show that there is a fairly large comparison of accuracy. Naïve Bayes with Forward Selection has an accuracy rate of 91.21%, higher than Naïve Bayes itself whose accuracy results are only 87.64%. there is a difference of 3.57%. Likewise, the accuracy of C4.5 with Forward Selection has an accuracy rate of 90.99%, higher than C4.5 alone which has an accuracy rate of 90.11%. there is a difference of 0.88%. So in the comparison between 4 algorithm model trials, Naïve Bayes and Forward Selection had the most dominant accuracy with an accuracy result of 91.21%.