<![CDATA[Within many small and medium-sized enterprises (SMEs) in the laundry sector, customer transaction data are predominantly utilized merely as historical records rather than being optimally processed for strategic business decision-making. Consequently, the potential of this data to elucidate customer behavioral patterns and inform targeted strategies remains largely untapped. This study aims to develop a customer segmentation feature for the Rayhan Laundry web application using Agglomerative Hierarchical Clustering (AHC). The method involved preprocessing transaction data, comprising customer identities, dates, and weights by generating unique IDs, handling missing values, and aggregating data based on total weight and transaction frequency. Utilizing Ward linkage and Euclidean distance, the AHC algorithm successfully partitioned customers into three distinct segments: high-volume, regular, and sporadic. The model achieved a silhouette score of 0.753, demonstrating robust cluster separation. The feature was seamlessly integrated into a Laravel-based web application, equipped with advanced search, filtering, pagination, and export functionalities. Functional validation via Blackbox Testing confirmed all features met specifications. Furthermore, usability evaluation using the System Usability Scale (SUS) yielded an average score of 82.75, categorized as “Excellent.” Conclusively, the developed feature is highly effective and user-friendly, providing a robust tool for data-driven customer relationship management in the laundry service industry.]]>
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