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All Journal Jurnal Informatika dan Teknik Elektro Terapan
Pangestu, Panji Indra
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Computer Science & IT

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ANALISIS SEGMENTASI PELANGGAN BERBASIS MODEL RECENCY FREQUENCY DAN MONETARY (RFM) MENGGUNAKAN ALGORITMA K-MEANS Pangestu, Panji Indra; Hermanto, Teguh Iman; Irmayanti, Dede
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3s1 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3s1.3396

Abstract

Business development is currently growing very rapidly, with the development of internet technology that can facilitate all business activities. Increasing business development has an impact on presenting new business competitors, so companies need strategies that are able to maintain customer quality. This study aims to segment customers from the company's sales transaction data, with a large number of transactions, technology is needed to group a data so that the method used in this study is a data mining method  and uses  the K-Means algorithm. With the K-Means Algorithm, it  can help in grouping customers to make it easier for companies to strategize each customer group. This customer grouping uses an initial model of Recency, Frequency and Monetary (RFM) to help calculate customer groups. Data mining evaluation  was carried out  using Silhouette Coefficient with test results using Visual Studio Code software python programming language, The results of this study selected 3 clusters consisting of Low Loyalty totaling 137 customers, Medium Loyalty totaling  1636 customers and Highest Loyalty totaling 2395 customers.
Co-Authors Dede Irmayanti Hermanto, Teguh Iman