Analisis probabilitas gagal bayar (problem loans) debitur menggunakan model regresi logistik biner

Jurnal Manajemen Strategi dan Aplikasi Bisnis

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Title Analisis probabilitas gagal bayar (problem loans) debitur menggunakan model regresi logistik biner
 
Creator Romantica, Krishna Prafidya
 
Description Credit distribution is an activity that dominates the bank's business in its function as an intermediary institution. In the process of lending, banks are often faced with a risk known as credit risk or problem loans. One of the causes of problem loans is the failure of banks to conduct credit analysis to prospective borrowers. The process of credit analysis of prospective debtors is carried out by coding the dummy variables involved in the research data. After that, the independent variable is estimated by its parameter value by maximizing the Likelihood function. The estimated value of the parameters was tested for significance by using the Likelihood Ratio test, the Wald test, and the Hosmer and Lameshow tests. At the 95% significance level, there are four independent variables that significantly affect problem loans, namely Age, Year_Emp, Income, and Debt_Income. The estimated parameter values of significance are substituted into the Binary Logistic Regression Model to determine the probability of debtor default.
 
Publisher Lembaga Pengembangan Manajemen dan Publikasi Imperium
 
Date 2019-09-17
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://ejournal.imperiuminstitute.org/index.php/JMSAB/article/view/87
10.36407/jmsab.v2i2.87
 
Source Jurnal Manajemen Strategi dan Aplikasi Bisnis; Vol 2 No 2 (2019); 155 - 164
2655-237X
 
Language eng
 
Relation https://ejournal.imperiuminstitute.org/index.php/JMSAB/article/view/87/54
 
Rights Copyright (c) 2019 Jurnal Manajemen Strategi dan Aplikasi Bisnis
http://creativecommons.org/licenses/by-nc-sa/4.0/
 

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