PENERAPAN PARTICLE SWARM OPTIMAZATION UNTUK MENEN-TUKAN KREDIT KEPEMILIKAN RUMAH DENGAN MENGGUNAKAN ALGORITMA C4.5
In studies that have been done previously to determine ownership loan home. One of the methods of the most widely used method with a high degree of accuracy is the C4.5 algorithm. In conducting this study also used a method algorithm C4.5 and to improve the accuracy will be performed using the addition of particle swarm optimization method for the determination of credit ratings. Homeownership after testing the results obtained is a support vector machine produces a value of 91.93% accuracy and AUC value of 0.860 was then performed using particle swarm optimization method in which the attributes which originally totaled 8 predictor variables selected from eight attributes used. The results showed higher accuracy value that is equal to 94.15% and AUC value of 0.941. So as to achieve an increased accuracy of 2.22% and an increase in AUC of 0.081. By looking at the accuracy and AUC values, the algorithm of support vector machines based on particle swarm optimization and therefore is in the category of classification is very good.
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