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M David
Universitas Bina Darma Palembang
Computer Science & IT

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Data mining M David; Diana Diana
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10418

Abstract

The purpose of this study is to provide recommendations to companies in determining stock levels based on previous sales data predictions using the K-Nearest Neighbor method. The data mining process carried out in this study follows the stages in Knowledge Discovery in Database (KDD) to produce information in accordance with a predetermined sequence consisting of data selection, preprocessing, transformation, data mining, and interpretation or evaluation. The tool used in the application of data mining is Python. The results of this study indicate the factors that influence product sales at PT. Alam Perkasa Lestari Palembang, including product price, sales (marketing), market competition, and others. The product sales prediction results based on the highest accuracy value are k=3168 with an accuracy of 97.00%. Thus, the KNN k=3168 algorithm method can be implemented to predict product sales at PT. Alam Perkasa Lestari Palembang.. Keywords : Data mining, K-Nearest Neighbor, Sales
Co-Authors Diana