Implementation of K-Means Clustering in Online Retail based on Recency, Frequency, and Monetary
| Main Authors: | Shaliha, Karima Marwazia, Angelyna, Angelyna, Nugraha, Arham Aulia, Wahisyam, Muhammad Humam, Sandi, Tri Kurnia |
|---|---|
| Format: | Article info application/pdf Proceeding |
| Bahasa: | eng |
| Terbitan: |
UIN Sunan Gunung Djati Bandung
, 2021
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| Subjects: | |
| Online Access: |
https://conferences.uinsgd.ac.id/index.php/gdcs/article/view/104 https://conferences.uinsgd.ac.id/index.php/gdcs/article/view/104/65 |
Daftar Isi:
- 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.