<![CDATA[Technological developments have had a significant impact on the banking industry, especially in facilitating access to information and financial services. This research implements the K-Nearest Neighbor algorithm for classification of loan applications at BMT Mu'amalah Syari'ah Tebuireng. This research focuses on efficiency and accuracy in the loan application evaluation process which was previously carried out manually. The data used includes customer information collected through interviews and observations. After going through the data preprocessing and normalization stages, the K-NN algorithm is applied to classify loan applications based on parameters such as age, employment, monthly income, dependents, collateral, residence, and credit status. The implementation of this algorithm has been proven to be able to speed up the evaluation process and reduce the risk of errors in decision making, thereby providing significant benefits in improving service quality and operational efficiency at BMT Mu'amalah Syari'ah Tebuireng. Keywords: K-Nearest Neighbor, Classification, Loan Application, BMT Mu’amalah Syari’ah Tebuireng]]>
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