Clustering Loyalitas Pelanggan Menggunakan Algoritma K-Means Berbasis Web
DOI:
https://doi.org/10.30865/resolusi.v4i6.2039Keywords:
Customer Loyalty; K-Means Clustering; Decision-Making EfficiencyAbstract
Active customer engagement in transactions with the company significantly impacts profitability. Categorizing customer data into loyal and non-loyal segments is a common method to identify loyalty patterns. The results of this segmentation can guide companies in designing follow-up strategies, including tailored incentives based on customer loyalty levels. Implementing a web-based K-Means Clustering algorithm allows PT Bhara Utama's management to easily access customer segmentation results, speeding up data analysis and enhancing decision-making efficiency. The use of web technology also facilitates integration with existing information systems and provides more flexible access. An experiment conducted on 660 customer data resulted in three groups: 8 very loyal customers, 461 moderately loyal customers, and 191 non-loyal customers. Accuracy evaluation using the Davies-Bouldin Index (DBI) showed a value of 0.19, indicating high-quality clusters.
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