IMPLEMENTASI DATA MINING PADA PREDIKSI PENJUALAN PRODUK TERLARIS DENGAN METODDE K-NEAREST NEIGHBOR
DOI:
https://doi.org/10.33884/comasiejournal.v11i4.9028Kata Kunci:
Data Mining; Euclidean distance; Jesindo Mitra Prakarsa Store; K-Nearest neighbor; PredictionAbstrak
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.
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Penulis Pertama, Khevind Adrian Pratama yang merupakan mahasiswa Prodi Teknik Informatika Universitas Putera Batam Mahasiswa yang aktif dalam bidang informatika
Penulis kedua, Koko Handoko, S. Kom., M. Kom, yang merupakan Dosen Pembimbing Prodi Teknik Informatika Universitas Putera Batam. Penulis Aktif sebagai tenaga kerja dan peneliti
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