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All Journal UNP Journal of Statistics and Data Science
Bimbim Oktaviandi
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Implementation of the Fuzzy C-Means Clustering Method in Grouping Provinces in Indonesia based on the Types of Goods Sold in E-commerce Businesses in 2022 Bimbim Oktaviandi; Tessy Octavia Mukhti; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss3/210

Abstract

The internet facilitates e-commerce by enabling efficient transactions and building consumer trust. With internet users in Indonesia reaching 204 million in 2022, it is crucial to Cluster provinces based on the types of goods and services sold online to design effective marketing strategies. The Fuzzy C-Means (FCM) method is used for Cluster analysis, allowing objects to have different membership degrees in multiple Clusters and providing accurate Cluster center placement. This study applies Fuzzy C-Means to Cluster 34 provinces in Indonesia based on the sale of goods/services in e-commerce in 2022, aiming to provide insights into market preferences and assist companies in developing more effective strategies. The results show that the method forms two Clusters. By evaluating standard deviation values and ratios, Fuzzy C-Means proves effective in Clustering provinces in Indonesia based on e-commerce sales data. Cluster validation reveals a standard deviation ratio of 0.14, indicating clear and significant Cluster separation.
Co-Authors Tessy Octavia Mukhti Yenni Kurniawati Zamahsary Martha