<![CDATA[The rapid development of online transportation services requires a data-driven understanding of customer behavior. This study aims to segment Maxim application customers using the Recency, Frequency, and Monetary (RFM) model combined with the K-Means clustering method among students of the Faculty of Engineering, Sultan Ageng Tirtayasa University. This research employs a descriptive quantitative approach with a sample of 100 respondents. The optimal number of clusters was determined using the Elbow method, resulting in four customer segments: Inactive Customers, Occasional Customers, Loyal Customers, and Priority Customers. The segmentation analysis was conducted separately for Maxim Bike and Maxim Car services. The results indicate that the Priority cluster has the highest transaction frequency and expenditure value despite consisting of relatively few customers, while the Inactive cluster shows the lowest level of transaction activity. In the Maxim Bike category, the Priority cluster represents the largest proportion of customers and shows the most recent transaction activity. In addition, the distribution of study programs indicates the dominance of Statistics students in the Loyal and Priority clusters across both service categories. Descriptive statistical analysis further shows that respondents' perceptions of Maxim services fall into the positive category, with average indicator scores above 3.20.]]>
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