Perbandingan Algoritma K-Means dan K-Medoids Clustering Penjualan Menu Restoran Bebek Cak Selem Gresik - Maratus Solikhah Tiara Wulan Sari; Dhian Satria Yuda Kartika; Agung Brastama Putra (Jurnal Teknik Informatika dan Teknologi Informasi, 2026)
Restoran Bebek Cak Selem Gresik in Gresik, East Java, recorded 11,339 transactions across 39 menu items from January to May 2025, yet this data had never been used as a basis for sales strategy. This study applies and compares K-Means and K-Medoids to group menu items by units sold and price, using the CRISP-DM framework. The optimal number of clusters was determined via the Elbow Method and evaluated using Silhouette Score, Davies-Bouldin Index, and Adjusted Rand Index (ARI). K=3 was found optimal, with both algorithms producing an identical Silhouette Score of 0.5368, Davies-Bouldin Index of 0.5861, and ARI of 1.0, proving both generate exactly the same cluster partition. Three menu segments were formed: 8 high-selling, 19 medium-selling, and 12 low-selling menus, each with a distinct sales-strategy recommendation, translated into an interactive HTML dashboard in owner-friendly language. K-Means is the more practical choice for restaurant-scale data, delivering equal clustering quality at substantially lower computation cost.