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
