Open AccessSupport Vector Regression for GPA Prediction
Author(s) -
Kania Evita Dewi,
Nelly Indriani Widiastuti
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/879/1/012112
Subject(s) - support vector machine , kernel (algebra) , regression , linear regression , regression analysis , computer science , statistics , simple linear regression , artificial intelligence , principal component regression , kernel method , proper linear model , mathematics , pattern recognition (psychology) , data mining , machine learning , polynomial regression , combinatorics
This study aims to predict student GPA. This research began by collecting data. The features used in predicting GPA are semester 1 and semester 1 IP grades. The process of GPA prediction uses SVM regression, Linear Regression, and Simple Linear Regression. Based on testing with normalized data, the smallest error is obtained by the SVM regression method with Kernel RBF which is equal to 0.1505. Whereas by using standardized data, the smallest error is obtained by using the SVM regression improve method with the Kernel RBF, which is 0.1487. Based on this research, in order to obtain prediction results that are closer to the actual values, it is better to standardize the data first and to predict the process using the SV Regression Improve method using the Kernel RBF.