<![CDATA[. In the development of business,credit problems remain tobe studie reveal edinteresting. Most problems the system imposed b ythe bank but the problem occur spreci selyt the human resources to manage credit, either on itsrelationship with the consumer or the mistake in leasing the wrong predictions in assessing consumers who apply for credit. Some computers have a lot offiel dresear chconductedto reduce the credit risk of causing harm to the company. In this study a comparison algorithm C4.5, KNN and theneural network which is appliedto the data consumer who gets the credit worthiness of motor good receptionis problematic in the install mentpaymentor not. The current methodhas not beenable to determinethe appropriatedata mining. The process of counting to three algorithms and programsadded with rapidminer can produce data that isaccurate and useful for all parties especially bess finance to further simplify the system in terms of determining the credit acceptan cevehiclesn results obtained C45 turns algorithmis more accuratein comparison witht woother algorithms. Keywords: C4.5, KNN, neural network, RapidMiner, Data Mining]]>
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