<![CDATA[Abstract— Credit success in a bank plays a role in maintaining the survival of a bank. Therefore it is very necessary to measure creditworthiness accurately to classify customers with good credit and bad credit. Based on these conditions the right data mining technique to use is classification. One of the data mining classification techniques is Naïve Bayes Classifier (NBC), but the accuracy is still less than the C4.5 algorithm and the neural network. This final report describes the steps of research using the Particle Swarm Optimizatin (PSO) algorithm to weight attributes to increase the accuracy value of C4.5. This study uses data set public German Credit Data. The validation process uses tenfold-cross validation, while testing the model using confusion matrix and ROC curve. The results show that the accuracy of C4.5 increased from 72.3% to 75.50% after being combined with PSO. Keywords: Credit, German Credit Data, C4.5-PSO. Keywords— Leaf image classification, cloves, shape, color, GLCM, PSO-SVM ]]>
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