Penerapan Clustering Terhadap Segmentasi Zonasi Gangguan Layanan Pelanggan Dengan Menggunakan Analisis RFM

  • Nurhadi Universitas Widyatama
  • Yan Puspitarani Universitas Widyatama
Keywords: clustering, data mining, customer segmentation, K-means, RFM

Abstract

The service area and the wide distribution of customers often require quite a long time in terms of handling disturbances, which can reduce the level of customer trust and loyalty to using the network services provided. Customer segmentation by region can help make it easier to analyze disruptions that occur to customer service in customer area zoning so that customer trust and loyalty can be maintained. This research was conducted to understand the characteristics of disturbances that occur in several customer zoning areas. In this study, RFM (Recency, Frequency, Monetary) analysis was performed. Clustering using the K-Means modeling technique, and also performance checking using Cluster Distance Performance to determine the performance of each cluster performed. The results of this study indicate that K-Means Clustering with a value of K=2 is the with the smallest Davies Bouldin value. It means that K=2 has the best performance when compared to other clusters formed.

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Published
2022-06-14
How to Cite
Nurhadi, & Puspitarani, Y. (2022). Penerapan Clustering Terhadap Segmentasi Zonasi Gangguan Layanan Pelanggan Dengan Menggunakan Analisis RFM. Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal), 9(1), 21-28. https://doi.org/10.38204/tematik.v9i1.900