Prediction of Student Study Duration Using Multiple Linear Regression Method - Fitri, Triyani Arita; Rahmawati; Lusiana; Rini Yanti (JAIA - Journal of Artificial Intelligence and Applications, 2023)
Data mining is a process of extracting valuable and meaningful information from large or complex data sets. In the field of education, data mining can be used to predict the length of study of students by identifying factors that affect the length of study of students. This research aims to predict the length of study of students and to find out the most influential variables in completing the length of study. The method used in this research is the Multiple Linear Regression method. Training data as much as 292 data is taken from data on graduates from 2016 - 2018. While the testing data is taken from the active student data class of 2018 as much as 148 data. The model formed will be evaluated to determine the accuracy and RMSE values. The results showed that the Multiple Linear Regression method succeeded in carrying out the prediction process optimally with a percentage accuracy value of 85%, and an RMSE value of 0.76, which means that the error rate of this model is very low. Based on the resulting coefficient value, the SKS variable is the most influential variable in the length of study of students.