Penerapan Metode Clustering Pada Penjualan Makanan Dan Minuman Dalam Menentukan Strategi Marketing - Evasaria Magdalena; Eufrasia Paskasius (Jurnal Nasional Teknologi dan Sistem Informasi, 2025)
Business competition including restaurant business is getting tighter with the change of business methods from conventional to online (digital). The problem is that the menu sold is not always comparable to the expenditure of raw materials purchased by the restaurant to make their food and beverage menus. Therefore, an appropriate marketing strategy is needed to be implemented. The restaurant used as the research material is the "Ben's Haus Bistro" restaurant. The attributes used consist of Item Name, Category Name, and Item Sold. In this study, one of the data mining methods will be applied, namely the clustering method using the K-Means algorithm to determine the marketing strategy. In addition, it will also be seen how much accuracy is obtained from the results of the clustering process that has been carried out. Based on the results of the accuracy level of the clustering process that has been carried out, a value of 0.6595 was obtained. While the results of applying the clustering method using the K-Means algorithm for overall sales data produced 4 clusters, because the results of the elbow method calculation to determine the optimal K value obtained the result K = 4. Cluster 1 obtained 6 items, Cluster 2 obtained 10 items, Cluster 3 obtained 43 items, and Cluster 4 obtained 52 items. From the results of the research that has been done, it can be concluded that the clustering method using the K-Means algorithm can be applied to food and beverage sales data to help determine marketing strategies.