PENERAPAN DATA MINING MENGGUNAKAN METODE K-MEANS CLUSTERING UNTUK ANALISA PENJUALAN TOKO UMAMA HIJAB KALIWATES JEMBER - Sofyan, Moh Sofyan Alfandi; Zaehol Fatah (Jurnal Riset Sistem Informasi, 2024)
Umama Hijab Kaliwates Jember Shop is a shop that specializes in selling hijabs. However, of the various hijabs being sold, not all of them sell well, some are less popular. Data on sales, purchases of goods and unexpected expenses at the Umama Hijab Store is not well structured, so the data only functions as store archives and cannot be used to develop marketing strategies. Therefore, it is necessary to apply data mining using the K-Means method at the Umama Hijab Shop. Data mining is the process of collecting and processing data to extract important information that supports decision making. The K-Means method can be applied to find out which hijab sales are selling well, selling well and not selling well. The application of this method is carried out by grouping hijab stock data. The process begins by selecting 3 random groups as initial centroids. After the data in each group did not change, the final results showed that there were 24 products that were selling well, 59 products were selling well, and 17 products were not selling well. Then, the application of the K-Means method in RapidMiner is carried out by entering product stock data, namely initial stock, sold stock and ending stock, which will be converted into a database in Ms. Excel. The data is then connected to RapidMiner Tools and processed to form groups using the K-Means algorithm. After that, RapidMiner produces product groups with high, medium and low demand.