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Gabrielle Jovanie Sitepu
President University
Computer Science & IT

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A Backpropagation Artificial Neural Network Approach for Loan Status Prediction Gabrielle Jovanie Sitepu; Edwin Setiawan Nugraha
JURNAL TEKNIK INFORMATIKA Vol 15, No 2 (2022): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v15i2.27006

Abstract

Providing credit has become a main source of profit for financial and non-financial institutions. However, this transaction might lead into credit risk. This risk occurred if debtors unable to complete their obligations that will led loss for creditors.  It is necessity for company to create assessment in distinguishing eligible or non-eligible prospective customer. Artificial Neural Network (ANN) is introduced in solving this typical classification case. Furthermore, one of learning algorithm in ANN namely Backpropagation is able to minimizing error of output in order to receive accurate result. This research aims to form models that capable in classifying the loan status of applicants by utilizing historical data. The method developed in this research is Backpropagation with activation function is a sigmoid function. In addition, this research formed two data model for analyzed; with first data model is every variable given in dataset and for the second data model is the variables that influenced the loan acceptance. Backpropagation shows high performance with more or less data variables. The results of this research show that the both data model has highest accuracy of prediction is 94.37% while the lowest accuracy prediction is 80.28%.
A Backpropagation Artificial Neural Network Approach for Loan Status Prediction Edwin Setiawan Nugraha; Gabrielle Jovanie Sitepu
JURNAL TEKNIK INFORMATIKA Vol 15, No 2 (2022): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v15i2.27006

Abstract

Providing credit has become a main source of profit for financial and non-financial institutions. However, this transaction might lead into credit risk. This risk occurred if debtors unable to complete their obligations that will led loss for creditors.  It is necessity for company to create assessment in distinguishing eligible or non-eligible prospective customer. Artificial Neural Network (ANN) is introduced in solving this typical classification case. Furthermore, one of learning algorithm in ANN namely Backpropagation is able to minimizing error of output in order to receive accurate result. This research aims to form models that capable in classifying the loan status of applicants by utilizing historical data. The method developed in this research is Backpropagation with activation function is a sigmoid function. In addition, this research formed two data model for analyzed; with first data model is every variable given in dataset and for the second data model is the variables that influenced the loan acceptance. Backpropagation shows high performance with more or less data variables. The results of this research show that the both data model has highest accuracy of prediction is 94.37% while the lowest accuracy prediction is 80.28%.
Co-Authors Edwin Setiawan Nugraha