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

Year: 2025

Type: article

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.

Source: Journal Of Informatics And Busisnes

DOI: 10.47233/jibs.v3i2.3106

Authors: Rio Rinto Saki, Rizky Juniarko Taruna Putra, Elkin Rilvani

Institution: Universitas Pelita Bangsa

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4415030573

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