<![CDATA[The Republic of Indonesia Employees' Cooperative (KPRI) was established as a legal entity based on the principles of family and people's economy, with a primary mandate to improve the welfare of its members. In its operations, savings and loan units are a crucial service. However, the cooperative's financial sustainability often faces serious challenges in the form of the risk of losses due to bad debts from debtors. This problem indicates that conventional methods for assessing prospective borrowers are often inaccurate and risk subjective, necessitating the need for stronger and more systematic criteria as a basis for decision-making. This research aims to address these issues by developing a Decision Support System (DSS) for loan eligibility. Through literature review and the collection of historical member transaction data, this research implements the K-Nearest Neighbor (K-NN) algorithm. This method was chosen for its ability to classify new loan eligibility based on similarity patterns (shortest distance) to previous customer data. The research results show that integrating the K-NN algorithm into the decision support system has a significant positive impact. The system has proven capable of providing classification recommendations that assist cooperative staff in processing loan applications according to predetermined criteria. System testing yielded a feasibility rate of 88%, indicating excellent performance. Overall, it can be concluded that the implementation of the K-NN method in KPRI loan approval processes makes the selection process more objective, accurate, and time-efficient compared to manual methods. This system is suitable for implementation as a strategic solution to minimize the risk of bad debt and maintain the financial stability of cooperatives.]]>
