Literatur Review: Alication of naive bayes for classification of students academic activities

Abstract

The rapid development of technology in the current digital era has a significant impact on various fields, including higher education. The use of information technology in universities, especially in the academic field for students, is crucial. Managing student data becomes one of the factors to improve the quality of educational information system services on campus. Student academic data includes grades, attendance, participation in activities, and personal information stored in the campus information system, which can be used to analyze students' academic achievements. With the information obtained, the campus can take action to prevent academic decline in students and design more effective and enjoyable learning strategies for students. This research aims to classify and predict improvements in students' academic performance using the Naive Bayes classification method. This method was chosen due to its simplicity and efficiency in processing data, as well as its ability to manage academic results. The outcomes of this research are also expected to support the development of decision support systems at the university and provide new insights for academic managers in designing policies and learning approaches that optimally support the improvement of students' academic performance.