New Student Prediction Using Algorithm Naive Bayes And Regression Analysis In Universitas Potensi Utama

Abstract

Universitas Potensi Utama has various study programs, and also has various facilities to support student learning activities. However, the main obstacle in higher education is the uncertainty of the interest of students who register, so that sometimes the facilities are inadequate. To overcome these problems, there must be activities to predict prospective new student applicants to improve facilities and interest of prospective students to choose to study at the Universitas Potensi Utama. Forecasting is the most important thing that must be applied to a company. With this forecasting, companies can see the opportunities that exist to generate sales predictions in the future based on the results of past sales data. Naive Bayes is a classification using probability and statistical methods, the Naive Bayes algorithm can be used in scientific fields, one of which is predicting future opportunities based on previous experience. Linear regression method can be used for forecasting with the assumption that the correlation between variables will continue in the future. Linear Regression is a Regression Method where the resulting equation is linear. Based on the resulting equation, predictions can be calculated by entering the values of the predictor variables in the equation. Based on this process, the predictive value of the response variable can be generated. In this study, the author explains more about how to determine new student predictions using a combination of the two algorithms where the Naïve Bayes algorithm is used to state which study programs have a lot of interest in the coming year, and the Linear Regression algorithm is used to show which study programs are most in demand based on the number of students who registered in the previous year, In the description that has been stated above, the author makes a paper with the title: “Predictions of New Student Registration Using the Naive Bayes Algorithm and Regression Analysis at the Universitas Potensi Utama”.