Pengelompokan Data Penjualan Produk Cetakan Pada Algoritma K-Means Dengan Bantuan Tool Orange - Susliansyah; Muhammad Ridho Caroko; Heny Sumarno; Hendro Priyono; Linda Maulida (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
The main problem faced is the large amount of unstructured sales data, making it difficult to perform manual analysis. With the application of the K-Means algorithm, sales data can be grouped into clusters representing products with high and low sales. The research process begins with the stages of problem identification, data collection, preprocessing, application of the K-Means algorithm, evaluation of clustering results, and then analysis and interpretation. Iteration results show that cluster C1 consists of a number of high-selling sales data, while cluster C2 encompasses the majority of low-selling sales data. Evaluation using the Davies-Bouldin Index (DBI) yields a value of 0.2818, indicating fairly good cluster quality, while the Silhouette Plot provides values of 0.082 for C1 and 0.276 for C2, indicating that cluster C2 is more stable compared to C1. Scatter Plot visualization shows the data distribution forming a slanted pattern from C1 to C2. The result of this research is that by using the K-Means algorithm, it can effectively cluster sales data of printed products, so it can be used as a basis for business decision-making related to marketing strategies, stock control, and product performance evaluation.