Healthcare services in Indonesia are considered inadequate, particularly in patient management at hospitals, which face significant challenges related to operational efficiency and service quality. With underlying issues such as limited capacity and resources, the variability of patient needs, and difficulties in predicting visit patterns, this research highlights the urgency of optimizing patient management through an innovative approach. This study aims to develop an effective patient management model by combining the RFM (Recency, Frequency, Monetary) method with Fuzzy C-Means. This approach is expected to provide a more accurate understanding of patient behavior and needs, enabling more effective resource allocation and improved service quality. The research methodology involves a literature review, data collection and preprocessing, clustering using the Fuzzy C-Means algorithm, cluster validation, patient characteristic analysis, and report preparation. Transaction and registration data from patients at Malahayati Hospital Medan, covering the period from January 2023 to March 2024, serve as the basis for the analysis
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