Simulasi dalam Optimalisasi Pengadaan Barang menggunakan Metode K-Mean Clustering - Indah Savitri Hidayat; Sarjon Defit; Gunadi Widi Nurcahyo (Jurnal Sistim Informasi dan Teknologi, 2021)
Products provided by a store have an influence on store sales. Consumers will be attracted to stores that provide products according to their wants and needs. The purpose of this research is to find out what ornamental flower products are most in demand by consumers, in demand by consumers and less desirable to consumers. Keywords: inventory of goods, K-Mean Clustering, Data Mining, cluster, optimal. Store managers can get information about goods that have been depleted of inventory stock to be updated immediately. The method used in this study is the K-Mean Clustering method which belongs to one of the branches of Data Mining. The data used in the study is data from January 2020 to December 2020 as many as 100 pieces taken from naafilah official shop, Padang. The data variables used in the entry of goods are the year, product name, price and amount sold. Furthermore, the data is processed using Rapid Miner software. The first stage of processing is to determine the value of clusters randomly, in this study researchers divided the cluster values into 3 groups. Next, the centroid value of each group will be determined. Centroid is derived from the minimum value, middle value and maximum value of the data provided. Then, the cluster process is calculated using the euclidean distance formula. Cluster calculations are done by calculating the closest distance to the data. The final result of this study is to find out the best-selling, best-selling and less-selling ornamental flowers, so that sellers can optimize the provision of ornamental flowers for the future.