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

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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

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Inventory Code Barcode Call Number Location Status
2507001180T156508T1565082024Central Library (REFERENCES)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1565082024
Publisher
Indralaya : Prodi Ilmu Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam.,
Collation
xiii, 64 hlm.; ill.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
510.07
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Matematika
Prodi Ilmu Matematika,
Specific Detail Info
-
Statement of Responsibility
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