Comparative study of bankruptcy prediction models

Isye Arieshanti*, Yudhi Purwananto, Ariestia Ramadhani, Mohamat Ulin Nuha, Nurissaidah Ulinnuha

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Early indication of Bankruptcy is important for a company. If companies aware of potency of their Bankruptcy, they can take a preventive action to anticipate the Bankruptcy. In order to detect the potency of a Bankruptcy, a company can utilize a model of Bankruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for Bankruptcy prediction. It is expected that the comparison result will provide insight about the robust method for further research. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP), Hybrid of MLP + Multiple Linear Regression), it can be concluded that fuzzy k-NN method achieve the best performance with accuracy 77.5%. The result suggests that the enhanced development of bankruptcy prediction model could use the improvement or modification of fuzzy k-NN.

Original languageEnglish
Pages (from-to)591-596
Number of pages6
JournalTelkomnika (Telecommunication Computing Electronics and Control)
Issue number3
Publication statusPublished - Sept 2013


  • Bagging nearest neighbour SVM
  • Bankruptcy prediction
  • Fuzzy k-NN
  • k-NN


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