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Optimizing Customer Segmentation in Online Retail Transactions through the Implementation of the K-Means Clustering Algorithm

Scientific Journal of Informatics · Vol. 11 No. 2 (2024) · hlm. 539–548

DOI
10.15294/sji.v11i2.6137
ISSN
2407-7658, 2460-0040
Terbit
2024-06-13
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

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.

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  • State University of Semarang
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Rekan penulisOrganisasiArtikel bersama
Angga Riski Dwi SaputraState University of Semarang2
Grace Yudha SatriawanState University of Semarang2
Salsabila Rahadatul’ AisyState University of Semarang2
Erwin PrasetyoState University of Semarang1
Nafa FajriatiState University of Semarang1
Much Aziz MuslimState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Endang SugihartiState University of Semarang1
Alamsyah AlamsyahState University of Semarang1
Riza ArifudinState University of Semarang1
Athifa Dihyan CalystaState University of Semarang1
Regina Ayumi UlayyaaState University of Semarang1
Barokah IsdaryantiState University of Semarang1
Isti HidayahState University of Semarang1
Lyna LatifahState University of Semarang1
FafuridaState University of Semarang1
Sabrina Desy RahmawatiState University of Semarang1
Edward JhonatanState University of Semarang1
Fariska Ratna FauziahState University of Semarang1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Optimizing Customer Segmentation in Online Retail Transactions through the Implementation of the K-Means Clustering Algorithm</dc:title>
	<dc:creator xml:lang="en">Awaliyah, Desi Adrianti</dc:creator>
	<dc:creator xml:lang="en">Budi Prasetiyo</dc:creator>
	<dc:creator xml:lang="en">Muzayanah, Rini</dc:creator>
	<dc:creator xml:lang="en">Lestari, Apri Dwi</dc:creator>
	<dc:subject xml:lang="en">Clustering</dc:subject>
	<dc:subject xml:lang="en">K-means</dc:subject>
	<dc:subject xml:lang="en">Online retail</dc:subject>
	<dc:subject xml:lang="en">Python</dc:subject>
	<dc:subject xml:lang="en">Customer segmentation</dc:subject>
	<dc:description xml:lang="en">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.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-06-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/6137</dc:identifier>
	<dc:identifier>10.15294/sji.v11i2.6137</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 11 No. 2: Regular Issue; 539-548</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/6137/537</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Scientific Journal of Informatics</dc:rights>
</oai_dc:dc>