Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-NEAREST NEIGHBOR (STUDI KASUS : RAKUNI BAKERY, PASTRY AND CAKE) - Sandy Aldy Pradana; Mochamad Adhari Adiguna (OKTAL : Jurnal Ilmu Komputer dan Sains, 2024)
n every shop, sales are very important so that the store can continue to operate and get income to be rolled back to buy materials or goods needed to be sold. Each store will compete through product quality and quality so that sales can continue to rise as expected. Store owners will usually use predictions on sales to find out how many products will be sold in the coming month. Data mining is a process that uses statistics, mathematics, artificial intelligence and machine learning to extract and identify useful information. Based on activity, data mining is collected into description, estimation, prediction, classification, clustering and association. The process in the data mining stage consists of three main steps, namely data preparation. In this step, data is selected, cleaned, and preprocessed following the guidelines and knowledge from domain experts who capture and integrate internal and external data into an overall organizational review. the information that has been generated after the data mining process must be displayed in a form that can be easily understood by the parties concerned. To predict sales of best-selling products, researchers use data mining calculations, which use classification techniques and the K-Nearest Neighbor algorithm, from the 10 products sold, the prediction results are obtained from RapidMiner Sales, which sell by Confusion Matrix testing, namely monitor burgers and chicken casio pao with accuracy values 85.00% and cross validation testing, namely monitoring burgers and girl pao dolls with an accuracy value of 87.00%.