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

Abstract

Education is a very important part of human life because through education quality human resources will be formed. Quality education can be read and measured by the achievement of various indicators. However, achieving these indicators is not easy, because learning success is influenced by several factors. One of the factors that can affect the success of learning is the learning system. To understand the level of student success in learning, a data mining processing technique is needed. The algorithm that will be used in this research is the naive Bayes algorithm. This study uses 601 datasets per year from Academic Year 2019/2020 to Academic Year 2021/2022, the data used are attendance score data, assignment scores, mid-exam scores, semester exam scores, and averages. The test is divided into 3, namely testing for the Academic Year 2019/2020 dataset, testing for the Academic Year 2020/2021 dataset, and testing for Academic Year 2021/2022 using the split validation operator. The test results using the Academic Year 2019/2020 – Academic Year 2020/2021 student score dataset have an accuracy value of 95.01% while the Academic Year 2021/2022 student score dataset has an accuracy value of 97.79%.