Application of Data Mining to Prediction of New Students' Interested Departements With an Approach Naive Bayes Algorithm
(1) Universitas Indraprasta PGRI
(2) Universitas Budiluhur Jakarta
(*) Corresponding Author
Abstract
This research aims to apply data mining techniques using the Naïve Bayes algorithm to predict new students' majors. Choosing a major is an important decision in college, and accurate predictions can help new students make better decisions. In this study, we collected historical data about past students, including information about academic values, interests, and other factors that influence major selection. The Naïve Bayes algorithm is used to process this data and produce a prediction model that can identify majors that best suit the characteristics of new students. The results of data processing for new students obtained accuracy values with the Naïve Bayes algorithm model of 98.55%, precision of 99.97%, and recall of 98.55%. The naive Bayes algorithm model obtained can be implemented in the form of an application designed to predict new students' majors in determining the study program they will take. The Naïve Bayes algorithm is able to provide fairly accurate predictions, which can be used as a guide for new students in choosing their major. This research makes a positive contribution to the development of data mining applications in the field of higher education, with the potential to help students and universities increase the efficiency of major selection. The Naïve Bayes algorithm is able to provide fairly accurate predictions, which can be used as a guide for new students in choosing their major. This research makes a positive contribution to the development of data mining applications in the field of higher education, with the potential to help students and universities increase the efficiency of major selection. The Naïve Bayes algorithm is able to provide fairly accurate predictions, which can be used as a guide for new students in choosing their major. This research makes a positive contribution to the development of data mining applications in the field of higher education, with the potential to help students and universities increase the efficiency of major selection.
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DOI: http://dx.doi.org/10.30998/faktorexacta.v17i2.20625
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