Penerapan Algoritma K-Means dan Apriori dalam Manajemen Stok UMKM Toko Sembako Berbasis Analisis BCG Matrix - Virdyra Tasril; Daffa Olivian; Randy Hasmajaya Simarmata (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
This study aims to analyze purchasing patterns at Toko Sembako HAS in Medan City, Medan Polonia District, using a Hybrid Data Mining approach that combines K-Means and Apriori algorithms. The dataset consists of 75,294 items sold over a 7-month period. The research workflow began with problem identification, literature review, data collection, and pre-processing, followed by algorithm implementation to produce product clustering and association patterns. Data normalization was performed using the Min-Max method to align the scales of Quantity and Profit, ensuring accurate K-Means clustering. The K-Means clustering combined with BCG Matrix categorized products into Stars, Cash Cows, Question Marks, and Dogs. Products such as Indomie and Mie Sedap were classified as Stars with high sales volume and medium-high profitability, while Minyak Curah and Beras were Cash Cows with moderate sales volume but the highest profitability. The Apriori algorithm revealed hidden purchasing patterns, with the highest Lift Ratio of 1.48 observed for the pair Pampers S and Mie Sedap, indicating a strong correlation within the young family segment. The hybrid approach provides strategic insights: K-Means supports inventory management and product segmentation, while Apriori guides marketing strategies such as product bundling and store layout. However, combinations of Cash-Cows and Question Marks yielded Lift Ratios below 1, indicating insignificant associations. The results demonstrate that this hybrid approach enhances understanding of consumer behavior and supports data-driven decisions to optimize sales and profitability.