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Pratama, Khevind Adrian
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Computer Science & IT

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IMPLEMENTASI DATA MINING PADA PREDIKSI PENJUALAN PRODUK TERLARIS DENGAN METODDE K-NEAREST NEIGHBOR Pratama, Khevind Adrian; Koko Handoko
Computer Science and Industrial Engineering Vol 11 No 4 (2024): Comasi Vol 11 No 4
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v11i4.9028

Abstract

Jesindo Mitra Prakarsa stores in Batam City include stores that offer a range of toys for kids. Lack of information about which items clients buy frequently or infrequently leads to an overabundance of inventory, which is a common issue for the store selling toys. Therefore, a projection that makes use of data or historical sales information is needed to assist the store in stocking the goods. The goal of this study is to use the K-Nearest Neighbor algorithm to predict the sales of the most popular toys among kids in Jesindo Partners Prakarsa stores. Data will be gathered through observation techniques, in-store interviews, and literature reviews relevant to the study's subjects. In order to forecast sales for the following month, the author used the Euclidean Distance formula with a value of k=3 and RapidMiner software to predict sales of the best-selling product, Tricycle Happy. The formula had a target of seven products, but it was predicted to sell as many as six. Based on the results of RMSE testing, which showed a value achieved close to zero at 2.035 +/- 0.000 means, the author's algorithm matched or was effectively applied to this study.
Co-Authors Handoko, Koko