Penerapan Data Mining Untuk Prediksi Penjualan Produk Pangan Hewan Menggunakan Metode K-Nearest Neighbor
DOI:
https://doi.org/10.30865/klik.v3i5.683Keywords:
K-Nearest Neighbor; Euclidean Distance; Animal FeedAbstract
Sales are buying and selling transactions for profit, in sales there is sales data or information about sales. Sales data is currently stored very much, it's just that it hasn't been used to help the sales process, including to predict the types of feed that are the most in-demand and not in demand. This can be used by the owner as an ingredient for making decisions in stocking food products. This research was conducted using animal feed sales data which aims to process information or data to become the basis for making conclusions for business owners in predicting sales using the data mining method, namely K-Nearest Neighbor. The KNN method itself has several advantages, one of which is that if the training data is large, the data will be more effective and resilient than noisy. This study uses 208 datasets with a comparison of the use of training data and testing data of 80:20. The results of this study resulted in an accuracy of 80.4% for classifying product categories with Euclidean Distance and produced predictions for the best-selling product that was the most sold, namely Whiskas 80gr Junior Tuna with predictions of 6963 sold. Meanwhile, the product that was predicted to sell the least was Whiskas Adult 1.2 kg with predictions sold as many as 8
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