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Non-performing loans has been one of the biggest problems in the banking sector. One alternative to minimize credit risk is to improve the evaluation of the applicant's credibility. Credit risk assessment methods must be improved. Credit scoring is an evaluation of the feasibility of credit requests. Poor credit can lead to an increase in non-preforming loans that may reduce bank productivity even in the event of financial crises and financial institutions bankruptcy. The number of Data-mining-based Credit scoring model has increased. The performance of classifiers in solving financial problem become the main reason why it is growing rapidly. Previously, credit scoring is based on the conventional statistics such as logistic regression and discriminant analysis. Eventhough those techniques produce a good accuracy, some of the assumptions cannot be accomplished by the data. Along the development of infromation technology, more advance approach named data mining has been developed. Therefore, this study performs Data Mining approach to solve NPL percentage problems in Bank. The classification methods that will be used is Decision Tree C4.5, Back Propagation Neural Network, and ensemble classifier algorithms. Classifier with the best accuracy is Decision Tree C4.5 with Adaboost with 98,87% The best sensitivity also performed by Decision Tree C.5 complemented by adaboost with 97,3%. It is considered as the best model in terms of prevent the type II error which could impact to the increase of non-performing loan in a bank.