<![CDATA[Abstract-The creditworthiness assessment carried out by the company is currently still conventional, namely still in paper form, so that Credit Analyst takes a long time to process data and analyze the large amount of incoming credit applicant data, so it does not rule out the possibility of miscalculations, errors in reading data, and others. In addition, the biggest obstacle is the difficulty in finding or storing archives that have been stored. As well as problems in making reports that are late sometimes also hamper the delivery of information to company leaders. For this reason, a technique that is capable of classification is needed. One of the algorithms included in the classification is Naïve Bayes. The system implementation uses Visual Basic.Net programming language and the method used in this research is waterfall method. The waterfall method is able to analyze the needs used to find out from weaknesses of the old system, then make a design and continue with the design of the new system. The conclusion from the results of this study explains that the process of classifying consumer eligibility with the Naïve Bayes method first goes through the training process. Prediction results are also strongly influenced by the amount of data that is trained and the probability value in this method.]]>
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