Enhancing Orchid Inventory Management with K-Means Clustering: A Case Study in Sales Optimization - Ramadhani, Dwi; Setiawan, Tabah Heri; Basir, Choirul; Sari, Dewi Purnama (Universitas Wiralodra, 2024)
In the ornamental industry, particularly in orchid sales, manual inventory management has presented significant challenges. These challenges include excessive stock accumulation, financial pressure due to overstocks, and limited land resources to store overstocks. Utilizing information technology, data mining, specifically the K-Means Clustering algorithm, emerges as a solution. This research aims to optimize orchid inventory management by grouping products into three clusters: