Analisis Segmentasi Pelanggan Berbasis Model Recency Frequency Dan Monetary (Rfm) Menggunakan Algoritma K-Means

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.

Penerapan K-Means Untuk Menganalisis Kepuasan Mahasiswa Terhadap Pembelajaran Daring

“…K-means memiliki kemampuan mengelompokkan data dalam jumlah besar dan waktu yang cepat. Pada K-Means objek harus dalam klaster tertentu akan tetapi dalam tahapan berikutnya objek akan berpindah ke kluster lain [8].…”