Pendekatan Analisis Klaster untuk Meningkatkan Manajemen Inventaris dan Rekomendasi Produk di Industri Tanaman Hias - Muhammad Iqbal Faturohman; Thalia Naziha (Jurnal E-Komtek (Elektro-Komputer-Teknik), 2024)
The ornamental plant industry has grown significantly as consumers seek to enhance living spaces with diverse plant species. This study aims to optimize inventory management and marketing strategies by applying K-Means clustering to categorize plants based on price, pot size, light requirements, care levels, and popularity. The method used is K-Means clustering, which groups plants into three clusters based on key characteristics. By analyzing these attributes, K-Means clustering identifies patterns and similarities among different plant species, allowing businesses to understand consumer preferences and inventory management better. The results identified three main clusters: Cluster 1 (moderate care, light, popularity) plants like Aglaonema require balanced stock and targeted promotion for medium-light environments. Cluster 2 (low care, light, popularity) plants such as Aglaonema Chiangmai need high stock levels and budget-friendly marketing. Cluster 3 (high care, light, popularity) plants like Alocasia demand elevated stock, premium quality, and care-focused promotion.