Classifications Using Artificial Neural Network Method In Protecting Credit Fitness

Elin Panca Saputra, Indriyanti Indriyanti, Supriatiningsih Supriatiningsih


Classification is information that has the closest relationship with data, we make a prediction in providing customer eligibility to get a loan from a financial service institution. In this study, we use the Artificial Neural Network (NN) method in combination with the Particle Swarm Optimization method. It is known that the method has excellent generalizations to solve a problem in increasing accuracy. However, some of the attributes in the data can reduce accuracy and increase the complexity of the Artificial Neural Network (ANN) algorithm. Therefore, attribute selection is very necessary, the attribute selection method used in this study is the Particle swarm optimization (PSO) method. This method can be used for proper attribute selection in determining lending to customers, therefore the Particle Swarm Optimization (PSO) method can increase the value of higher accuracy weights in determining attribute selection.


ANN-PSO Classification Credit

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