Implementation of K-Means Clustering in Online Retail based on Recency, Frequency, and Monetary

Karima Marwazia Shaliha (Teknik Informatika, UIN Sunan Gunung Djati Bandung)
Angelyna Angelyna (Teknik Informatika, UIN Sunan Gunung Djati Bandung)
Arham Aulia Nugraha (Teknik Informatika, UIN Sunan Gunung Djati Bandung)
Muhammad Humam Wahisyam (Teknik Informatika, UIN Sunan Gunung Djati Bandung)
Tri Kurnia Sandi (Teknik Informatika, UIN Sunan Gunung Djati Bandung)

Gunung Djati Conference Series
Vol. 3 (2021): Mini Seminar Kelas Data Mining 2020

During a pandemic like today, many changes have occurred, one of which is the increasing number of online buying and selling sites. Each Online Store offers a variety of products and services with a variety of attractive offers, competing fiercely to attract enthusiasts. With the occurrence of a pattern of change in society, it is necessary to carry out a grouping to obtain information in order to determine a better sales strategy. The grouping process uses techniques from data mining, namely Clustering with the K-Means algorithm based on the Recency Frequency Monetary (RFM) algorithm, it is hoped that by analyzing the three attributes and implementing the K-Means algorithm, it can provide an accurate output and in accordance with the objectives of this study.