Published: 2023-12-30
Utilizing Clustering Methods for Categorizing Delivery Requirements Based on Analysis of E-Commerce Product Data
DOI: 10.35870/ijsecs.v3i3.1969
Jumat Azzam Sugiarto, Suprapto, Muhamad Fatchan
Abstract
This study presents the implementation of the K-Means algorithm model, revealing novel insights into risk categorization in the delivery process. Two distinct clusters were identified: Cluster 1 (C0) indicating high risk, comprising 53 data points out of a dataset of 360, and Cluster 2 (C1) indicating low risk, encompassing 307 data points from the same dataset. Analysis conducted using RapidMiner Studio corroborated these findings, further delineating the cluster membership: C0 with 53 data points and C1 with 307 data points. Each cluster was characterized by optimal centroid values, recorded at 131.717 & 385.075 for C0, and 119.932 & 111.414 for C1. The model's effectiveness was assessed using the Davies-Bouldin Index, yielding a value of 0.626.
Keywords
Data Mining ; K-Means ; Clustering ; E-Commerce ; Product Analysis
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Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 3 No. 3 (2023)
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Section: Articles
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Published: December 30, 2023
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License: CC BY 4.0
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Copyright: © 2023 Authors
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DOI: 10.35870/ijsecs.v3i3.1969
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Jumat Azzam Sugiarto
Informatics Engineering Study Program, Faculty of Engineering, Universitas Pelita Bangsa, Karawang Regency, West Java Province, Indonesia
Suprapto
Informatics Study Program, Faculty of Engineering, Universitas Pelita Bangsa, Karawang Regency, West Java Province, Indonesia
Muhamad Fatchan
Informatics Study Program, Faculty of Engineering, Universitas Pelita Bangsa, Karawang Regency, West Java Province, Indonesia
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