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Henny Yulianti
Prodi Informatika, Universitas Siber Asia
Computer Science & IT

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Segmentasi Anggota Koperasi Berdasarkan Extend-RFM (Recency, Frequency, Monetary) Menggunakan Algoritma K-Means Henny Yulianti
MULTINETICS Vol. 12 No. 1 (2026): MULTINETICS Mei (2026)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v12i1.8101

Abstract

Cooperatives are economic institutions that play a vital role in Indonesia’s economy, with their number continuing to grow in line with the increasing public interest. However, cooperatives face challenges in maintaining member loyalty due to limited monitoring of transaction behaviors and the rising competition among cooperatives. This study aims to cluster cooperative members based on transaction patterns using the extended-RFM (Recency, Frequency, Monetary) model with additional variables of Number of Items and Total Profit, applying the K-Means algorithm. The optimal number of clusters was determined using the Elbow method and Silhouette Score, while cluster quality was evaluated through the Davies-Bouldin Index (DBI). The results show that the best clustering consists of four clusters, with a Silhouette Score of 0.395 and a DBI of 0.885. The Analytical Hierarchy Process (AHP) indicates that Total Profit has the highest weight, whereas Number of Items has the lowest. The clustering categorizes members into Platinum, Gold, Silver, and Bronze segments, each with different contribution levels. These findings are expected to serve as a reference for cooperatives in formulating more effective marketing strategies and enhancing member loyalty.
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