Abstraksi
Every day there is a transaction process performed by Customer. The
process generates a lot of data where there are 82,648 transactions from
the month of January-December 2017. This study aims to perform customer
segmentation on Nine Reload Credit by utilizing data mining process
based on RFM model and by using techniques Clustering. The algorithm
used for cluster formation is K-Means algorithm. K-Means produces a
visual cluster model with the Rapidminer 5.2 tools that represent the
number of customers in each cluster by using RFM (Recency, Frequency,
and Monetary) attributes. From 82,648 transactions that were then
processed, based on RFM model it resulted in 102 Customers. Furthermore,
we analyzed cluster by using K-Means algorithm with the result of 63
Customers in Cluster 1 and 39 Customers in Cluster 2. The result of this
research can be used by company to know customer category, and then the
company will know how to maintain the customer owned.
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