Abstract

Financing analysis is the process of analyzing the ability of bank customers to pay installments to minimize the risk of a customer not paying installments, which is also called Non-Performing Financing (NPF). In 2020 the NPF ratio at one of the Islamic banks in Indonesia increased due to the decline in people’s income during the Covid-19 pandemic. This phenomenon has led to bad banking performance. In December 2020 the percentage of NPF was 17%. The imbalance between the number of good-financing and NPF customers has resulted in poor classification accuracy results. Therefore, this study classifies NPF customers using the Logistic Regression and Synthetic Minority Over-sampling Technique Nominal Continuous (SMOTE-NC) method. The results of this study indicate that the logistic regression with SMOTE-NC model is the best model for the classification of NPF customers compared to the logistic regression method without SMOTE-NC. The variables that have a significant effect are financing period, type of use, type of collateral, and occupation. The logistic regression with SMOTE-NC can handle the imbalanced dataset and increase the specificity when using logistic regression without SMOTE-NC from 0.04 to 0.21, with an accuracy of 0.81, sensitivity of 0.94, and precision of 0.86.

Original languageEnglish
Pages (from-to)115-128
Number of pages14
JournalInternational Journal of Advances in Soft Computing and its Applications
Volume13
Issue number3
Publication statusPublished - 2021

Keywords

  • Classification
  • Islamic Bank
  • Logistic Regression
  • Non-Performing Financing
  • SMOTE-NC

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