Comparative Study of Bankruptcy Prediction Models

Abstract:  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.
Keywords: Bankruptcy prediction, k-NN, fuzzy k-NN, Bagging Nearest Neighbour SVM, Hybrid method MLP+ Multiple Linear Regression
Authior: Isye Arieshanti, Yudhi Purwananto, Ariestia Ramadhani, Mohamat Ulin Nuha, Nurissaidah Ulinnuha
Journal Code: jptkomputergg130088

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