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

  • Krishna Prafidya Romantica Fakultas Bisnis, Institut Teknologi dan Bisnis Kalbis, Jakarta, Indonesia×
Keywords: problem loans, the Binary Logistic Regression Model, coding the dummy variables

Abstract

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.

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Published
2019-09-17
How to Cite
Romantica, K. (2019). Analisis probabilitas gagal bayar (problem loans) debitur menggunakan model regresi logistik biner. Jurnal Manajemen Strategi Dan Aplikasi Bisnis, 2(2), 155 - 164. https://doi.org/10.36407/jmsab.v2i2.87
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Articles