<![CDATA[In the process of determining capital loans to customers, checking customer data to find out several criteria, namely checking OJK slik, customer age, customer status, customer income in one month and type of guarantee. These criteria will determine whether the customer gets the loan funds or not. The problem that arises is because there is no computerized system in determining capital loans, so in determining the loan it is still using manual or personal assessments. Meanwhile, Bank Syariah Indonesia prioritizes data that is really accurate in lending capital to customers. The method used in determining the borrowing of the customer's capital is the K-NN (K-Neareast Neighbor) method. The reason is that this method is a method used for classification in finding the closest distance between the data to be evaluated and its nearest neighbor. The author collects data by conducting questions and answers or consultations that are carried out directly to the leadership of the Indonesian Islamic Bank Cikarang Branch to find out the obstacles that exist in the bank regarding information related to research. Calculation of the K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on (K) their closest neighbors. K-NN is a supervised learning algorithm, where the results of a new query instance are based on the results of the K-NN. The class that appears the most will be the class resulting from the classification. Create a customer loan determination system that can calculate the value accurately, whether the customer deserves to be borrowed capital or not and also makes a value classification based on the data available to the customer. Determine the maximum limit for each of the existing criteria.]]>
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