Application Of K-Means Clustering Algorithm to Identify the Best-Selling Digital Printing Services - Ana Fatahali Ramadhan; Sudin Saepudin; Carti Irawan; Mupaat (Indonesian Journal of Data and Science, 2025)
The digital printing industry in Indonesia is experiencing rapid growth thanks to the increasing demand from companies for printing services such as banners, stickers, brochures, and business cards. CV. Copy Paste is one of the companies operating in the digital printing industry that fulfills various printing orders every month. However, the company has difficulty identifying the most popular printing services, which makes it difficult to develop a targeted promotional strategy. In view of this problem, the aim of this study is to group digital printing services according to their popularity using the K-Means Clustering method. This study uses a quantitative approach, collecting sales data from the last 12 months, covering 160 types of services. The steps taken include preliminary data processing, namely attribute selection, data cleaning, and data transformation so that it can be effectively processed using the K-Means algorithm, implemented in the Python programming language. The test results show that digital printing services can be divided into three clusters: 115 less popular services (C1), 31 fairly popular services (C2), and 14 very popular services (C3). The results of this study provide information that can be used as a basis for strategic decisions regarding promotion and service management. In this way, the K-Means Clustering algorithm has proven effective in helping companies group products in a more objective and measurable way based on historical data.