Abstract
Churn is a customer's behaviour to quit using product or service and then move to another company. Customer churn is serious problem that must be handled to keep company survive. Handling of churn customers starts from predicting which customers will churn. One approach in predicting customer churn is data mining. This study compares two classification methods in data mining namely artificial neural networks and decision trees in banking industry case. The result showed that the artificial neural network has accuracy 86% and precision 71%, which is better than the decision tree. However, the decision tree has a better recall value than the artificial neural network, which is 58%..
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