<![CDATA[Bad credit is the main problem that faced by financial institutions, especially cooperatives in Indonesia. This problem is also happened in KSP Mitra Raya Wates that does not use credit analyst and the decision making process is using an intuitive approach and based on existing experience that owned by KSP Leader. The survey process conducted at KSP Mitra Raya Wates also cannot guarantee that the loans made by customers are free from credit risk, considering there are customers who have bad credit from a total of all customers who have received loans. KSP Mitra Raya Wates needs a system that capable of supporting decision to detect credit quality early on. C4.5 method can be used to predict customers' credit quality by generating rule in form of decision tree. The results of confusion matrix have accuracy of 94.5946. While based on the ROC curve, it generated AUC value of 0.9689. The level of usability generated by utilizing SUS is 82.5. The output is dashboard visualization with several graphs containing the percentage, time-series and trend of total submissions that have been made and also forms that can be used by KSP Mitra Raya Wates to make predictions of customer credit application and also dataset entry into the system.]]>
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