Penggunaan Metode DTW Pada K-Means Dalam Menganalisis Tren Penjualan Produk - Laode Izat Trianto Haradin; Arief Hermawan; Donny Avianto (Jurnal Informatika dan Komputer, 2026)
Data is a collection of raw information that can be in the form of symbols, numbers, or words. Supermarkets are a type of modern market that functions as an intermediary between producers and consumers. Along with the increasing convenience of services and payment systems, sales transaction volumes have also increased. Based on this, this study proposes the use of the K-Means algorithm combined with Dynamic Time Warping (DTW) to cluster sales trend patterns. The main purpose of using DTW is to enable the comparison of sales time series that have shifting patterns, thus resulting in a more representative clustering process. The results of the clustering evaluation show that the K-Means configuration with the number of clusters K = 3 produces a Davies-Bouldin Index (DBI) value of 3.119, which indicates a relatively good level of cluster separation and compactness. This finding has important significance because it shows that the DTW-based K-Means approach is able to reveal meaningful sales trend patterns and can be used as a basis for strategic decision-making, such as stock planning, promotions, and more optimal supermarket sales management. Thus, the results of this study imply that the DTW-based K-Means approach can be used as an alternative method for analyzing sales patterns in the retail sector. These findings are expected to assist supermarket management in understanding sales behavior, supporting strategic decision-making, and improving the effectiveness of future inventory and promotional planning.