Research Article
The Comparison of Ridge Regression Method and Lasso Regression Method to Predict The Graduation Time
@INPROCEEDINGS{10.4108/eai.21-9-2023.2342995, author={Humaira Humaira and Nikita Chairunnisa and Novi Novi and Yulia Jihan Sy and Rika Idmayanti}, title={The Comparison of Ridge Regression Method and Lasso Regression Method to Predict The Graduation Time}, proceedings={Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis, Riau, Indonesia}, publisher={EAI}, proceedings_a={ABEC}, year={2024}, month={2}, keywords={prediction graduation time ridge regression lasso regression mse}, doi={10.4108/eai.21-9-2023.2342995} }- Humaira Humaira
Nikita Chairunnisa
Novi Novi
Yulia Jihan Sy
Rika Idmayanti
Year: 2024
The Comparison of Ridge Regression Method and Lasso Regression Method to Predict The Graduation Time
ABEC
EAI
DOI: 10.4108/eai.21-9-2023.2342995
Abstract
Every year, graduates from the department of information technology are produced. There are not as many graduates as there are new students each year. This is as a result of the high rate of late graduations. The Department of Information Technology has a problem with this. Making an intelligent system is the answer to these issues. Intelligent system to estimate students' graduation times. Data from past years' graduations of students was gathered and trained. Utilizing regression to train on training data. The two types of regression are compared in this article: Ridge and Lasso. The prediction model's outputs had an accuracy of 92.22% and an MSE of 0.084, which is the best possible result. Ridge Regression, which produces the best prediction model, was used. Using its coefficients, Lasso Regression can identify the factors that have the greatest impact on the desired value.
