Penerapan Algoritma K-Means dalam Segmentasi Anggota Koperasi Berdasarkan Pola Simpanan dengan Analisis RFMP untuk Meningkatkan Loyalitas
DOI:
https://doi.org/10.47065/bulletincsr.v6i1.843Keywords:
Cooperative; K-Means Clustering; Data Mining; Elbow Method; SavingsAbstract
Cooperatives as member-based financial institutions have an important role in supporting the welfare of their members. However, the diversity of member characteristics, both in terms of length of membership and the amount of monthly savings, poses challenges in formulating effective management strategies. This study aims to group cooperative members based on financial transaction data patterns using a data mining approach. The method used is the K-Means Clustering algorithm, with the main variables using RFMP analysis (Recency, Frequency, Monetary and Payment), namely length of membership (Recency), frequency of savings in a year (Frequency), amount of monthly savings (Monetary) and timely loan payments (Payment). Data is processed through a pre-processing stage, the data is normalized using the Min–Max Scaling method to equalize unit differences between variables, Determination of the optimal number of clusters is done using the Elbow Method, which shows that the best number of clusters is three groups. The results of the study with a total of 50 transaction data resulted in cooperative members being divided into three clusters. The first cluster is at-risk members at 36%, the second cluster is potential members at 40%, and the third cluster is loyal or exclusive members at 24%. These findings provide practical solutions for cooperatives in developing member management strategies. Existing members need to be motivated to increase savings, new members need coaching to foster loyalty, and premium members need special services to maintain their satisfaction. Thus, clustering results can form the basis for data-driven decision-making in cooperative management.
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Copyright (c) 2025 Abdul Razak Naufal, Turkhamun Adi Kurniawan, M Al’Amin
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