Implementasi Algoritme Naïve Bayes Berbasis Particle Swarm Optimization Dalam Penentuan Pemberian Kredit

Abstract

Lease (leasing) or often abbreviated as SGU is afinancing activityis the provision of both capital goods lease with option rights (finance lease) and lease (lessee) for a certain period based on payment in installments, this activity can be said the activities of credit, credit is the provision of money or bills that can be equated with it, based on an agreement between a lending financial institution to another party requiring the borrower to pay off the debt after a certain period with interest. Submission of loan applications by prospective customers are now very easy, this is because the loan application can be done by all people at all certain eligible. Competition credit providers to be very rapid and predictions of consumer credit is very important. Of this problem requires a model that is able to classify as well as predict which customers are problematic and not problematic. Naive Bayes Model based Particle Swarm Optimization (PSO) was able to increase the accuracy in analyzing the credit worthiness, the larger the dataset used the Naive Bayes model accuracy based Particle Swarm Optimization (PSO) will increase, the accuracy obtained by this model to the data set used are: Agiing 2010 (96.75%), Agiing 2011 (97.95%), Japan credit approval (84.77%) and Australia credit approval (87.83%). Keywords: Credit analysis, credit assessment, Naive Bayes based PSO. I.