Optimization of Perfume Sales through Data Mining with K-Means Algorithm - Rahayu, Mia Setya; Yunita, Ika Romadoni; Widiawati, Chyntia Raras Ajeng (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2024)
This time the research used the abc Parfume shop as the research site. This store offers various types of perfumes with different variants because, there are many variants so that not all perfumes sell quickly and some even do not sell at all. To recap sales and expenses in abc stores is still done manually so that it often causes mistakes in increasing stock and hinders the development of marketing strategies. The data that has been collected should be used as a decision-making system to solve business problems. For this reason, the author conducts data mining calculations that are carried out automatically in the hope of providing effective and maximum results in analyzing perfume sales at abc perfume stores. The application of Data Mining in collaboration with the K-Means Algorithm has proven to provide the best analysis and be a solution in developing the perfume business. The results of this study divided the clustering into three clusters for the final result there were nine cluster projects with nine products, cluster two with three products, and cluster three or the last cluster with thirteen products from a total of twenty-five data collected. The results of each cluster are grouped such as Cluster One which is the best seller, Cluster two is grouped to the middle position because sales are stable, while products in Cluster Cluster three are less in demand. This research was successfully conducted and contributed to a deeper understanding of the K-Means algorithm.