BibTex Citation Data :
@article{J.Gauss26751, author = {Reno Wardhani and Sudarno Sudarno and Di Maruddani}, title = {OPTIMASI REGRESI LOGISTIK MENGGUNAKAN ALGORITMA GENETIKA UNTUK PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI PENGGOLONGAN KREDIT BANK (Studi Kasus: Debitur di PT BPR Gunung Lawu Klaten Periode Tahun 2017)}, journal = {Jurnal Gaussian}, volume = {8}, number = {4}, year = {2019}, keywords = {credit classification, logistic regression, genetic algorithms}, abstract = { Credit is the greatest asset managed by banks and also the most dominant contributor to the bank’s income. But in its implementation, the provision of credit to the public is at risk for non-performing loans. For this reason, creditors try to minimize the occurrence of non-performing loans by predicting credit risk appropriately. In this study, modeling the factors that influence credit classification at PT BPR Gunung Lawu is useful for predicting the credit risk of prospective debtors. Modeling are done using logistic regression and genetic algorithms. Factors suspected of influencing credit classification include age, gender, marital status, education, home ownership, employment, net income, tenor, type of business, type of loan, type of loan interest, and loan size. Estimated model parameters obtained from logistic regression were optimized using genetic algorithms. The fitness function used is pseudo or and MSE. The best model is generated by modeling with genetic algorithms based on MSE fitness. The model produces the highest value of 0.1958 and the lowest MSE value of 0.1648 with classification accuracy of 75.33%. Keywords: credit classification, logistic regression, genetic algorithms }, issn = {2339-2541}, pages = {506--517} doi = {10.14710/j.gauss.8.4.506-517}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/26751} }
Refworks Citation Data :
Credit is the greatest asset managed by banks and also the most dominant contributor to the bank’s income. But in its implementation, the provision of credit to the public is at risk for non-performing loans. For this reason, creditors try to minimize the occurrence of non-performing loans by predicting credit risk appropriately. In this study, modeling the factors that influence credit classification at PT BPR Gunung Lawu is useful for predicting the credit risk of prospective debtors. Modeling are done using logistic regression and genetic algorithms. Factors suspected of influencing credit classification include age, gender, marital status, education, home ownership, employment, net income, tenor, type of business, type of loan, type of loan interest, and loan size. Estimated model parameters obtained from logistic regression were optimized using genetic algorithms. The fitness function used is pseudo or and MSE. The best model is generated by modeling with genetic algorithms based on MSE fitness. The model produces the highest value of 0.1958 and the lowest MSE value of 0.1648 with classification accuracy of 75.33%.
Keywords: credit classification, logistic regression, genetic algorithms
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