Segmentasi Berbasis Data Time Series Penjualan Produk Kopi Menggunakan Algoritma K-Means - Anggaini, Meri; Herlawati, Herlawati; Purnomo, Rakhmat (Universitas Muhammadiyah Magelang, 2025)
Coffee shops are businesses in the Food and Beverage (F&B) sector that contribute 7.15% to Indonesia's economy. The high demand for coffee has led to increasingly fierce competition. Kanae Coffee & Space in Bekasi faces challenges in maintaining customer loyalty and managing unpredictable demand. This study aims to apply the K-Means algorithm to cluster coffee products based on time series sales data, using the 6-step CRISP-DM methodology. The number of clusters was determined using the elbow method and confirmed with a silhouette coefficient of 0.5916 (good structure). The analysis resulted in five clusters with distinct characteristics: Cluster 0 (very low demand, stable trend, very high price), Cluster 1 (very high demand but sharply declining trend, very low price), Cluster 2 (moderately high demand, moderately stable trend, moderate price), Cluster 3 (moderate demand, slowly declining trend, moderately high price), and Cluster 4 (low demand, stable trend, moderately low price). These segmentation results are expected to serve as the basis for more effective marketing strategies and product management.