Data Mining Analysis in Predicting the Number of New Students at IT&B Indonesia Using Linear Regression Method

Source abstract

This study explores the application of data mining techniques, particularly Multiple Linear Regression, to predict the number of new students at IT&B Indonesia based on historical admission data from 2010/2011 to 2021/2022 academic years. The findings indicate that the Multiple Linear Regression model achieved a 64% accuracy rate with a mean square error of 8.335, demonstrating its effectiveness in estimating new student admissions. Additionally, the developed system can be utilized to predict student performance based on predefined criteria, providing valuable insights for educational institutions in forecasting future admissions.