Analisis Kinerja Algoritma C4.5 Dan Naïve Bayes Dalam Memprediksi Keberhasilan Sekolah Menghadapi Un

Abstract

The national exam (UN) is one of the determinants of student graduation, both elementary school, junior high school and even high school. There are many businesses that are carried out by schools to prepare their students to face national examinations. In fact almost all schools provide material deepening to their students for subjects tested at the national examination. Therefore, this study was conducted to determine the level of success of the school in preparing students in facing national examinations. The method used is a decision tree with C4.5 algorithm and naïve Bayes algorithm. From the results of the study, the results of the accuracy of the naïve bayes algorithm were as big as 95,50% , while accuracy using the c4.5 algorithm is equal to 78,50%. Then it can be concluded that the predictions generated from the naïve bayes algorithm are better compared to the c4.5 algorithm .

Application of Data Mining using Naive Bayes for Student Success Rates in Learning

“…Based on testing with two datasets in Academic Year 2019/2020 -Academic Year 2020/2021 with Academic Year 2021/2022 with attributes of attendance scores, mid-semester test scores, semester scores, assignment scores and average scores with these attributes, grades with grade C have higher results than students who scored with grade A and grade B. in the research conducted (Anggraini et al, 2020) determining the success rate of schools facing the UN exam resulted in an accuracy of 95.50% with the average score of english having the potential to get an overall score for student graduation [14]. Meanwhile, the research (Irma et al, 2021) determined the success of online learning to produce an accuracy value of 99% with adaptive subject attributes that are the dominant factor, which include adaptive subjects, namely English, Mathematics, Science, Chemistry, Physics, Social Studies [12].…”

Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results

“…There were earlier studies linked to the research to be done. As for example the naive bayes classifier's prediction of stekom semarang student graduation rate produced an accuracy of 95.14% [10]. Then, another study suggested the use of naive bayes classifier's method to identify the success rate of schools preparing students for national exams resulted in 95% accuracy [11].…”