DESIGN OF KIP KULIAH SELECTION SYSTEM AND RECIPIENT DETERMINATION USING SUPPORT VECTOR MACHINE (SVM)
Abstract
KIP Kuliah is tuition assistance from the government for high school graduates or equivalent with good academic potential but has economic limitations. In recent years it has been seen that the Indonesian government has always tried to increase the quota for KIP Kuliah recipients. In this study, the Support Vector Machine (SVM) method was applied to create a system for selecting and determining KIP Kuliah recipients. To obtain the best model to be used in the system, the training and testing data are divided into three data distribution schemes, namely 60/40, 70/30, and 80/20. After the training and testing process was carried out using the SVM method with various parameter variations, then the best accuracy rate of 94.59% is obtained in the 80/20 data sharing scheme for the nonlinear SVM model with the RBF kernel. With this system, it is hoped that the KIP Kuliah selection process at the tertiary level can run effectively, efficiently and the results of the determination are more targeted.
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