<![CDATA[This study aims to predict receivables data at Bank Syariah Indonesia Tbk for the 2021-2023 period using regression and clustering methods. Data analysis methods such as regression and clustering have been used to predict credit risk and receivables payment behavior. A linear regression model is applied to predict the future value of different types of receivables (Murabahah, Istishna, Multijasa, Qardh, Serent), while K-Means clustering is used to group data based on five main variables. The results of the analysis show that the linear regression model is able to predict future values with quite good accuracy, shown by the compatibility between the actual value and the predicted value. K-Means clustering produces three fairly good clusters, with a silhouette score of 0.51, which indicates adequate cluster quality. Visualization of the results of the analysis shows the distribution and patterns in the data, providing insight into the relationships between different types of receivables. This research provides a deeper understanding of the structure of receivables data and aids in decision-making based on future predictions and data grouping]]>
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