Intelligent Segmentation of Cooperative Members Using a Hybrid Clustering Approach - Sudiantara, I Wayan; Saraswati, Ni Wayan Sumartini; Udayana, I Putu Agus Eka Darma (International Journal of Informatics and Computation, 2026)
Member management in savings and loan cooperatives faces challenges due to heterogeneous member characteristics and increasing credit risk. Member segmentation is essential for data-driven decision-making; however, the mixed nature of cooperative data, which consist of both numerical and categorical attributes, limits the effectiveness of single clustering methods. This study proposes a hybrid clustering framework that sequentially integrates K-Means and K-Modes to generate more comprehensive and interpretable member segments. K-Means is first applied to identify patterns in numerical attributes, whereas the resulting cluster labels are incorporated into K-Modes to enhance the clustering of categorical attributes. The optimal number of clusters was determined using the Elbow Method and Silhouette Analysis. A case study was conducted using 3,216 member records from a savings and loan cooperative, containing demographic, financial, and transactional characteristics. The experimental results indicate that K-Means produced three optimal clusters with a silhouette score of 0.237, while the hybrid framework generated four final member segments with clearer operational interpretations: loyal high-value members, low-risk growth members, seasonal agribusiness members, and economically vulnerable members. The findings demonstrate that the proposed hybrid clustering approach provides more comprehensive and actionable segmentation than single-method clustering, thereby supporting cooperative credit risk management, service development, and data-driven decision-making.