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Segmentasi pelanggan menggunakan algoritme bisecting k-means berdasarkan model recency, frequency, dan monetary (RFM)

Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

Department of Informatics, Universitas Mulawarman, Indonesia

Received: 5 Mar 2019; Revised: 14 Nov 2019; Accepted: 18 Nov 2019; Published: 30 Apr 2020; Available online: 5 Feb 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
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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Dataset -- Customer segmentation using bisecting k-means algorithm based on recency, frequency and monetary (RFM) model
Subject bisecting k-means; customer segmentation; RFM; best cluster; silhouette coefficient
Type Data Set
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Keywords: bisecting k-means; customer segmentation; RFM; best cluster; silhouette coefficient
Funding: Universitas Mulawarman, Indonesia; PT. Dinar Energi Utama

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Last update: 2021-06-22 14:33:19

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Last update: 2021-06-22 14:33:22

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