Implementasi Data Mining K-Means Clustering Untuk Pengelompokan Produk Keramik Berdasarkan Frekuensi, Volume, dan Jangkauan Penjualan - Ferdian Arya Dinata; Alwis Nazir; Fadhilah Syafria; Teddie Darmizal; Eka Pandu Cynthia (Bulletin of Computer Science Research, 2026)
Ceramic inventory management at CV. Makmur Bersama has generally relied on intuition or partial sales data, without accounting for purchasing behavior patterns as a whole. This approach simultaneously creates two major risks: overstocking of slow-moving products, which burdens working capital and storage space, and stockouts of high-demand products, which can result in lost sales opportunities. This problem is further compounded by the limitation of stock data, which typically contains only a single quantitative variable such as the number of units sold and is therefore unable to comprehensively capture product demand characteristics, such as how frequently a product is purchased or how broad its customer base is. As a result, restocking decisions and promotional strategies are often poorly targeted. This research applies the K-Means algorithm to cluster ceramic products based on historical sales patterns as a solution to this limitation. Historical sales data from CV. Makmur Bersama for the 2025 period, consisting of 6,328 transactions, was processed into 417 unique products through a feature engineering approach using Frequency, Monetary, and Reach (FMR) namely transaction count, total quantity sold, and unique customer count per product. After outlier detection using the Interquartile Range (IQR) method, 381 products remained for the clustering process. The optimal number of clusters was determined using the Elbow Method, resulting in k=4 as the best cluster count. Evaluation using the Davies-Bouldin Index (DBI) produced a value of 0.8954, categorized as good, and stability testing across five iterations with different random states showed consistent results (DBI standard deviation of 0.0034). The clustering results produced Cluster 1 (190 products, 49.9%) as slow-moving products, Cluster 2 (34 products, 8.9%) as top-performing products with an average transaction frequency of 30.8 times, Cluster 3 (93 products, 24.4%) as potential products, and Cluster 4 (64 products, 16.8%) as products with limited demand. This research provides practical contributions for companies in determining restocking priorities, promotional strategies, and working capital efficiency based on actual sales patterns. This research contributes methodologically through the adaptation of the RFM framework into FMR to better suit real-world data constraints, as well as the integration of the Elbow Method, Davies-Bouldin Index, and stability testing as a comprehensive validation mechanism. Practically, the segmentation results can be directly utilized by the company as a basis for restocking priorities, promotional strategies, and working capital allocation efficiency based on actual sales patterns.