Implementation of the Density-Based Spatial Clustering (DBSCAN) Method to Identify Beauty Clinic Customer Purchasing Patterns
DOI:
https://doi.org/10.69916/jkbti.v5i3.559Keywords:
DBSCAN, Customer Segmentation, RFM Analysis, Beauty Clinic, Pattern RecognitionAbstract
Beauty clinics routinely accumulate detailed transaction records, yet these data are often used only for administrative reporting and are not transformed into actionable customer insights. This study implements Density-Based Spatial Clustering of Applications with Noise (DBSCAN) in a web-based system to identify customer purchasing patterns at Raen Aesthetic. Transaction data from January to December 2025 were cleaned, aggregated into recency, frequency, and monetary features, and normalized using a robust scaler. DBSCAN was executed with epsilon = 0.8000 and minimum samples = 5, while cluster quality was assessed using the Silhouette Score and Davies-Bouldin Index. The implementation produced nine customer clusters and 148 noise observations. The Silhouette Score of 0.3551 indicated a reasonably structured separation, whereas the Davies-Bouldin Index was 1.2569. Cluster 1 was the largest segment, comprising 355 customers with low purchase frequency and relatively small monetary value. Cluster 5 represented a high-value segment with an average monetary value of approximately IDR 10,302,000, while the noise group displayed heterogeneous but economically significant transaction behavior. Treatment transactions dominated most clusters, whereas several segments showed stronger preferences for packages and deposits. Black-box testing confirmed that all major functions operated according to the specified scenarios. The results demonstrate that DBSCAN can provide interpretable customer segmentation and support evidence-based service and customer-management decisions in beauty clinics.
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