KOMPARASI METODE K-MEANS DAN K-MEDOIDS CLUSTERING UNTUK PENGELOMPOKAN PELANGGAN BERDASARKAN ANALISIS RECENCY, FREQUENCY, MONETARY

FITRIANI, RISA and Resti, Yulia and Suprihatin, Bambang (2024) KOMPARASI METODE K-MEANS DAN K-MEDOIDS CLUSTERING UNTUK PENGELOMPOKAN PELANGGAN BERDASARKAN ANALISIS RECENCY, FREQUENCY, MONETARY. Undergraduate thesis, Sriwijaya University.

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Abstract

Customer transaction data is very important for companies to monitor sales increases and can be used to group customers based on their characteristics. This data includes information about items sold, quantities, item names, prices, and customer names. In this research, 1.948 customer transaction data were clustered at CV Mahkota Jaya Bersama. Customer grouping through Recency, Frequency, Monetary (RFM) analysis and effective Clustering Methods are used to help the Company win competitive market competition. In this research, Recency, Frequency, Monetary (RFM) analysis with a comparison of the K-Means and K-Medoids Clustering methods can provide benefits, namely Recency, Frequency, Monetary (RFM) analysis helps understand customer behavior, while K-Means and K- Medoids Clustering groups customers based on complex characteristics in common. The results of this research after evaluation using Elbow, Silhouette Coefficient, and Davies Bouldin Index concluded that the K-Means Clustering Method had better quality in forming clusters, namely 3 clusters. Meanwhile, the clustering results that are formed can be used to divide customers into characteristics, namely Best Customers, Loyal Customers and Lost Customers. Keywords: K-Means, K-Medoids, RFM, Customer Grouping

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	K-Means, K-Medoids, RFM, Pengelompokan Pelanggan
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Risa Fitriani
Date Deposited:	02 Sep 2024 02:45
Last Modified:	02 Sep 2024 02:45
URI:	http://repository.unsri.ac.id/id/eprint/156508

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