Application of Data Mining to Prediction of New Students' Interested Departements With an Approach Naive Bayes Algorithm

This research makes a positive contribution to the development of data mining applications in the field of higher education, which has the potential to help students and universities improve the efficiency of major selection.The aim of this research is to apply data mining techniques using the Naïve Bayes algorithm to predict new students' majors.Accurate predictions can help new students make better decisions.Data obtained is based on historical data about past students, including information about academic grades, 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 method using the Naïve Bayes algorithm forms a grouping model using the Confusion Matrix matrix table and classifies positive and negative values.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 quite accurate predictions, so it can be used as a guide for new students in choosing their major.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%.