CLUSTERING PELANGGAN MENGGUNAKAN METODE K-MEANS BERDASARKAN MODEL RECENCY, FREQUENCY, MONETARY (RFM) UNTUK KEPUASAN PELANGGAN PADA OUTLET MIXUE (STUDI KASUS: OUTLET MIXUE SAKO PALEMBANG)

MARZUKI, CHOSMAS and Utami, Alvi Syahrini and Satria, Hadipurnawan (2023) CLUSTERING PELANGGAN MENGGUNAKAN METODE K-MEANS BERDASARKAN MODEL RECENCY, FREQUENCY, MONETARY (RFM) UNTUK KEPUASAN PELANGGAN PADA OUTLET MIXUE (STUDI KASUS: OUTLET MIXUE SAKO PALEMBANG). Undergraduate thesis, Sriwijaya University.

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Abstract

Outlet Mixue in Sako District, Palembang City, is one of the attractive fresh tea and ice cream outlets in Indonesia. However, after the pandemic, the outlet faced challenges in its marketing strategy and ineffective customer satisfaction, which affected its performance. Despite being popular and having many visitors, there are still challenges in attracting potential customers through ineffective promotions and a lack of effort in building a strong brand image. Therefore, this study aims to identify and analyze the results of customer clustering at Outlet Mixue in Sako based on the model Recency, Frequency, Monetary (RFM) using the K-Means method, as well as applying the K-Means algorithm to cluster Outlet Mixue customers in Sako. Primary data was obtained directly from Outlet Mixue in Sako Palembang, consisting of 730 people with 9 attributes consisting of information on Date, Customer Name, Product Type, Product Quantity, Total Price, Payment Method, Age, Gender, and Domicile. The test results showed that the optimal number of clusters for Outlet Mixue customer segmentation is 10 clusters, based on the Davies Bouldin Index (DBI) which has the lowest value of 0.61, indicating optimal cluster quality. In addition, this study successfully developed customer clustering software that can identify and analyze the results of customer clustering at Outlet Mixue in Sako based on the RFM model using the K-Means method. Therefore, this study contributes to the development of customer clustering software and provides in-depth insights into the behavior of Outlet Mixue Palembang customers based on the RFM model.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Customer Clustering, K-Means, RFM Model, Customer Satisfaction.
Subjects:	T Technology > TS Manufactures > TS155-194 Production management. Operations management > TS183.G76 Manufacturing processes, Automation. Production control. CAD/CAM systems. Computer integrated manufacturing systems. Robots, Industrial.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Chosmas Marzuki
Date Deposited:	02 Jan 2024 08:55
Last Modified:	02 Jan 2024 08:55
URI:	http://repository.unsri.ac.id/id/eprint/137338

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