ALGORITMA KLASIFIKASI SUPPORT VECTORE MACHINE BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT KOPERASI
Jurusan Teknik Informatika, STT Nurul Jadid Probolinggo
Email : firstname.lastname@example.org
Credit analysis done by analysts sometimes inaccurate, so some credit given the debtor has no ability to pay that cause bad credit. Of this problem we need a model that is able to classify as well as predict which troubled borrowers and not problematic. By applying Decision Tree algorithm based on support vector machine (PSO) based particle swarm optimization (PSO) is expected to improve the accuracy of credit analysis. Of the existing problems as well use a classification method to predict creditworthiness that is the model of support vectore machine algorithm based particle swarm optimization. From the research, the determination of credit worthiness using lagoritma support vector machine based on particle swarm optimization is able to analyze Non-performing loans and are not troubled as much as 94.17 %. Credit analysis is performed using support vector machine algorithm based particle swarm optimization is more accurate than on the analysis done by an analysis that is sometimes inaccurate.To see better accuracy using support vector machine algorithm based particle swarm optimization than using credit analysis is done manually .
Keyword : Credit analysis, Algorithm support vectore mahine, PSO