Paper Title
Using Artificial Neural Network (Ann) Backpropagation to Predict The Bankruptcy of Islamic Banks in Indonesia

Abstract
As a business entity, the Islamic banks also cannot free from financial distress and the bankruptcy. The economy conditions cannot be known certainty and the presence of open competition both in national and international become a major concern of issues of Islamic banking in Indonesia. The research objectives are to define the prediction of bankruptcy in Islamic Banking to the banking industry in Indonesia by utilizing ANN and check the consistency, mention the failed banks and success banks by the prediction results, and explain the factors must be improved to avoid the failure. The data used in this research are published by the Islamic banks and the conventional banks in Indonesia. Since there are not failure Islamic banks in Indonesia nowadays, so this research use the data of conventional banks. Quarterly calculations of the financial ratios will be processed in the MATLAB R2014a version with neural network backpropagation approach. From the results, can be known that the average of accuracy of the networks in predicting the failed bank group is 98.5% and 100% for the success bank group in the training process. From 12 banks, the network trained indicates one bank as the failed bank. There are three banks which must pay attention to their two financial ratios. Then, there are three banks which each of them must improve one ratio. Lastly, five banks predicted success in all ratios. The result of robustness test is the networks could predict the success bank group with perfectly corrects prediction. Index Terms- Islamic banks in Indonesia, bankruptcy, prediction method, Artificial Neural Network (ANN)- Backpropagation