Segmentasi Permintaan Pelanggan Paket Dekorasi Wedding Menggunakan K-Means Berbasis Web
DOI:
https://doi.org/10.47065/bulletincsr.v6i6.1361Keywords:
Customer Segmentation; K-Means Clustering; Wedding Decoration Packages; Demand Analysis; Web-Based SystemAbstract
This study aims to analyze customer demand segmentation for wedding decoration packages using a web-based K-Means algorithm at PT. WPD. The research problem is the manual processing of historical customer data, which requires considerable time and may cause errors in grouping. The dataset consists of 250 historical customer records randomly selected from 500 transaction records for the January–December 2025 period to obtain a representative sample and accelerate model testing without reducing the variation in customer characteristics. The modeling attributes include package price or budget, decoration theme, number of add-ons, and venue scale, with categorical attributes (decoration theme and venue scale) converted into numerical values through label encoding before all numerical attributes were normalized using Min-Max Normalization. The research stages consist of observation, interviews, literature study, data collection, preprocessing, normalization, cluster determination (K=3) using the Elbow method based on the Within-Cluster Sum of Squares (WCSS) value, K-Means implementation, web-based system implementation, and testing. The analysis produced three customer segments: an economy/budget segment containing 60 customers, a middle/personalization segment containing 50 customers, and a premium/exclusive segment containing 140 customers. The premium segment is the largest group and is dominated by customers choosing the Classic International Elegance package with high budgets, extensive add-ons, and large-scale venues. The web-based system successfully automates data processing and clustering and provides analytical results to support evaluation and marketing strategies. A questionnaire involving 20 respondents on user acceptance and perceived usefulness of the system resulted in a score of 88%, categorized as very strong, reflecting user acceptance of the system rather than the accuracy of the K-Means algorithm.
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