Segmentasi Pelanggan Menggunakan Fuzzy C-Means dan FP-Growth Berdasarkan Model LRFM untuk Rekomendasi Produk

 (*)Astriana Rahmah Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 M Afdal (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: May 24, 2024; Published: July 26, 2024

Abstract

Bazmart Pelalawan is a part of the National Zakat Agency (BAZNAS) program in Pelalawan Regency, which has implemented strategies to retain customers. However, these strategies have not yet succeeded in fully understanding customer characteristics, resulting in a decline in customer trust and their willingness to shop again. Additionally, Bazmart lacks proper guidelines for offering products that meet customer needs. This research aims to enhance product recommendations by integrating LRFM analysis into data mining techniques. The parameters considered include customer LRFM values, customer segmentation, and products frequently purchased together over a year of transaction data. Fuzzy C-Means and FP-Growth algorithms were used for segmentation and association analysis. The segmentation results identified two customer clusters with a Davies-Bouldin Index (DBI) value of 0.628, indicating good cluster quality. In the association analysis, a minimum support (minsup) of 30% and a minimum confidence (mincof) of 70% were used, resulting in 8 rules for cluster 1 and 17 rules for cluster 2. From the two association pattern results, the highest rules were obtained, namely in Drinks and Snacks and Bread with a support value of 0.426 and a confidence value of 0.926 resulting in a value of 0.394. These rules provide insights that Bazmart Pelalawan can use to develop more effective and targeted direct marketing strategies for each customer cluster. Thus, this research is expected to help Bazmart Pelalawan better understand customer characteristics and improve customer loyalty through more targeted product recommendations.

Keywords


Customer Segmentation; FP-Growth; Fuzzy C-Means; LRFM; Product Recommendation

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