Implementasi Data Mining dengan Metode K-Means dan FCM untuk Analisis Pola Pembelian Konsumen Online

This study explores the implementation of data mining techniques using K-Means and Fuzzy C-Means (FCM) clustering methods to analyze online customer purchasing patterns. The focus of the analysis lies in identifying similarities and segmenting customers based on their transaction behaviors. By using datasets collected from e-commerce platforms during the 2022–2023 period, the study evaluates the effectiveness of each algorithm in discovering meaningful clusters. The results indicate that both methods can group consumers based on purchasing trends, with FCM offering better flexibility due to its fuzzy membership assignment. This clustering approach can support decision-making in targeted marketing, product recommendations, and customer relationship management.

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Publication Details

Journal
Journal Of Informatics And Busisnes
Published
2025-08-13
DOI
https://doi.org/10.47233/jibs.v3i2.3106
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Implementasi Data Mining dengan Metode K-Means dan FCM untuk Analisis Pola Pembelian Konsumen Online

Rio Rinto Saki, Rizky Juniarko Taruna Putra, Elkin Rilvani
Journal Of Informatics And Busisnes
Data Mining and Machine Learning Applications
article

Implementasi Data Mining dengan Metode K-Means dan FCM untuk Analisis Pola Pembelian Konsumen Online

Rio Rinto Saki, Rizky Juniarko Taruna Putra, Elkin Rilvani
article en

Abstract

This study explores the implementation of data mining techniques using K-Means and Fuzzy C-Means (FCM) clustering methods to analyze online customer purchasing patterns. The focus of the analysis lies in identifying similarities and segmenting customers based on their transaction behaviors. By using datasets collected from e-commerce platforms during the 2022–2023 period, the study evaluates the effectiveness of each algorithm in discovering meaningful clusters. The results indicate that both methods can group consumers based on purchasing trends, with FCM offering better flexibility due to its fuzzy membership assignment. This clustering approach can support decision-making in targeted marketing, product recommendations, and customer relationship management.

Journal Of Informatics And BusisnesVol. 3(2)
Universitas Nusa Bangsa (ID), Universitas Pelita Bangsa
Openalex Percentile: Top 59%
Data Mining and Machine Learning Applications
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