Peer Review: Jurnal Nasional Terakreditasi_Widians_Segmentasi pelanggan menggunakan algoritme bisecting k-means berdasarkan model recency, frequency, dan monetary (RFM)
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Date
2020-04-30Author
Puspitasari, Novianti
Widians, Joan Angelina
Setiawan, Noval Bayu
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Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132.
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