Predicting Student’s Cumulative Grade (IPK) using Linear Regression with Variation in Testing Data Size
DOI:
https://doi.org/10.58526/jsret.v4i1.675Keywords:
Cumulative Grade, Study Time, Linear Regression, Testing Data SizeAbstract
Higher education is a crucial part in the education of Indonesian youngsters. Some of the most important part of higher education for students as well as for institutions are cumulative grade (IPK) and study time. The purpose of this study is to find the relation between study time and cumulative grade, or more precisely to predict cumulative grade based on study time. This prediction is done using the linear regression formula. The data are collected from the academic database of Faculty of Teacher Training and Education, Malikussaleh University. The data are then preprocessed and split into training data and testing data. The size of the testing data is varied to find the most optimal one. After training and testing, it was found that the most optimal size of testing data is 6%, which resulted in Root Mean Square Error (RMSE) of 0.1481 and R-squared (R2) of 0.6109. Though the result of these metrics are relatively worse compared to previous studies using linear regression, it can still be used to help find the optimal test size. To achieve better metrics, more data need to be collected from longer range of years.
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