Customer Segmentation Using K-Means Clustering with RFM Method (Case Study : PT. Dewangga Travindo Semarang)

Authors

  • Hida Sekar Winaryanti University of Dian Nuswantoro
  • Heru Pramono Hadi University of Dian Nuswantoro
  • Eko Hari Rachmawanto University of Dian Nuswantoro

DOI:

https://doi.org/10.33633/jais.v9i1.10440

Abstract

PT. Dewangga Travindo is a company that operates in the field of travel services which includes tours, travel, and Hajj and Umrah pilgrimages which is based in the city of Semarang and has received permission from the Ministry of Religion No. D/606 of 2013. Every year there is always an increase in sales of services. Hajj and Umrah. The higher transaction activity every day results in a large buildup of data in the database which will only become data waste. The ability to process data is increasingly sophisticated using data mining, which is an activity of looking for relationships between items to obtain patterns as information to assist in decision making. However, considering the large number of competitors offering the same services, it is necessary to increase competitiveness to overcome market segmentation at PT Dewangga Travindo. For this reason, this research was carried out which aims to overcome customer segmentation using the Clustering method with the K-Means algorithm which produces a visual cluster model with RStudio tools using RFM attributes applied to carry out segmentation. The data used in this research is data on Hajj and Umrah pilgrims in the 2018-2020 period.

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Published

2024-04-02

Issue

Vol. 9 No. 1 (2024): Journal of Applied Intelligent System

Section

Articles

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