<![CDATA[The Srikandi Savings and Loans Cooperative is a microfinance institution that plays an important role in supporting the economic needs of its members through savings and loan services. Along with the annual increase in the number of members and transactions, the cooperative still processes savings and loan transaction data manually using members’ cash books. In addition, financial report preparation is conducted through manual calculations and recapitulation, which is time-consuming and prone to calculation errors. Therefore, an effective and accurate data management system is required to improve cooperative operational efficiency. This study implements a website-based data mining approach using the K-Means clustering method to process savings and loan transaction data. The K-Means method is applied to group member transaction data based on similar characteristics, such as deposit frequency and loan amounts. The clustering results are expected to support cooperative management in decision-making processes, including determining loan limits, evaluating member loyalty, and developing strategies to improve service quality.]]>
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