Optimasi Parameter Support Vector Machine dengan Algoritma Genetika Untuk Penilaian Resiko Kredit

Abstract

The aim of this study is to optimize the parameters of a Support Vector Machine (SVM) using a genetic algorithm for credit risk assessment. Consumer credit data from a bank is used in this research. The results show that the SVM with parameters optimized using a genetic algorithm provides better classification performance compared to the SVM with default parameters. In addition, the genetic algorithm can also quickly and efficiently optimize SVM parameters. In conclusion, the genetic algorithm can be used to optimize SVM parameters for credit risk assessment

Keywords: Support Vector Machine (SVM), Parameter optimization, Genetic algorithm, Credit risk assessment, Classification performance