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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Soft Computing Exploration Journal of Student Research Exploration Scientific Journal of Informatics
Budi Prasetiyo
Computer Science Department, FMIPA, Universitas Negeri Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : Scientific Journal of Informatics

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Optimizing Customer Segmentation in Online Retail Transactions through the Implementation of the K-Means Clustering Algorithm Desi Adrianti Awaliyah; Budi Prasetiyo; Rini Muzayanah; Apri Dwi Lestari
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.6137

Abstract

Purpose: The main objective of this research is optimal use of customer segmentation using the Recency, Frequency and Monetary (RFM) approach so that companies can better understand and comprehend the needs of each customer. By carrying out this segmentation, companies can communicate better and provide services tailored to each customer. Methods: The K-means algorithm is used as the main method for customer segmentation in this research. This research uses a dataset of online retail customers. Apart from that, this research also uses the elbow method to help determine the best number of clusters to be created by the model. Result: Based on the elbow method, the most optimal is to use 3 clusters for this case. Thus, in K-means modeling, forming 3 clusters is the best choice. Clusters produce groups of customers who have specific characteristics in each cluster. The analysis shows that quantity and unit price have a significant influence on online retail customer behavior. Novelty: This research strengthens the trend of using the K-means algorithm for customer segmentation in online retail datasets, which has proven popular in journals from 2018 to 2022. This research creates 3 new variables that will be used by the model to understand the characteristics of customer transaction behavior. This study also emphasizes the importance of exploratory data analysis in understanding data before clustering and the use of the elbow method to determine the most appropriate number of clusters, providing a significant contribution in analyzing customer segmentation.
Co-Authors Afrizal Rizqi Pranata Alamsyah Apri Dwi Lestari Cecep Bagus Suryadinata KA Desi Adrianti Awaliyah Erwin Prasetyo Hakim, M. Faris Al Hendi Susanto Hilda Vember Jumanto Unjung Much Aziz Muslim Much Aziz Muslim Niswah Baroroh Rini Muzayanah Rofik Rofik Tasya Fitria Anggraini Yosza Dasril