Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Yosia Oktavian Pailan
Universitas Jenderal Achmad Yani
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Segmentasi Loyalitas Pelanggan Berbasis RFM (Recency, Frequency, Monetary) Menggunakan K-Means pada PD. Persada Ikan Yosia Oktavian Pailan; Yulison Herry Chrisnanto; Asep Id Hadianna
Prosiding SISFOTEK Vol 4 No 1 (2020): Vol 4 No 1 (2020): SISFOTEK 2020
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (457.406 KB)

Abstract

Customer loyalty for the company is very important if the competition between similar companies is high enough which results in a threat to the company. Customer loyalty is very useful to determine the level of customer loyalty to the company. Customer segmentation is also needed to group customers who have the same characteristics into one so as to simplify the management process. The analysis used is the RFM (Recency, Frequency, Monetary) model to analyze customer buying behavior in terms of Recency (last transaction time span), Frequency (number of transactions), and Monetary (rupiah issued). The grouping method used is K-Means. The data used in this study are historical data on fish feed purchases from 2015 to 2017. The application of RFM analysis and the K-Means method resulted in 4 clusters based on Elbow calculations. The results in this study obtained the number of objects in cluster 1 as many as 142 customers, cluster 2 as many as 28 customers, cluster 3 as many as 41 customers and cluster 4 as many as 41 customers. The accuracy level of the cluster is measured using Silhoutte Coeffisien with results close to 1 which means the clustering is quite good. The interpretation of the RFM shows that 16.27% of customers have a high potential for loyalty, while 11.11% of customers have the potential as loyal customers, and the remaining 56.35% have a low level of loyalty. It can be concluded that this research can classify the level of customer loyalty using RFM analysis and the K-Means algorithm.
Co-Authors Asep Id Hadianna Yulison Herry Chrisnanto