MODEL JARINGAN SARAF TIRUAN UNTUK EVALUASI RESIKO KREDIT

Novan Wijaya

Abstract


Credit risk evaluation is an importanttopic in financial risk management and become a major focus in the banking sector. This research discusses a credit risk evaluation system using an artificial neural network model based on backpropagation algorithm. This system is to train and test the neural network to determine the predictive value of credit risk, whether high riskorlow risk. This neural network uses 14 input layers, nine hidden layers and an output layer, and the data used comes from the bank that has branches in EastJakarta. The results showed that neural network can be used effectively in the evaluation of credit risk with accuracy of 88% from 100 test data

Keywords


neural network, back propagation, credit risk

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DOI: http://dx.doi.org/10.24912/computatio.v2i1.1915

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Copyright of COMPUTATIO : JOURNAL OF COMPUTER SCIENCE AND INFORMATION SYSTEMS (P-ISSN : 2549-2810  E-ISSN : 2549-2829)


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Fakultas Teknologi Informasi

Faculty of Information Technology, Universitas Tarumanagara
Gedung R Lantai 11
Jl. Let.Jend. S.Parman No. 1 Jakarta 11440

 

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