Implementasi Data Mining Untuk Prediksi Penjualan Produk Terlaris Di Alfamart Panglima Polim II Menggunakan Metode K-Nearest Neighbor

Abstract

This research aims to implement data mining using the K-Nearest Neighbor method to predict the best-selling products at Alfamart Panglima Polim II. Currently, the store manager relies on subjective analysis based on previous month's sales data, which is ineffective and can lead to excessive stock accumulation. Therefore, this research proposes the use of data mining and the Knearest neighboralgorithm as a more effective approach to determine products that are frequently purchased by consumers. Data mining will be used to analyze previous sales patterns and identify relationships among the existing sales data. The K-Nearest Neighbor method will be applied to classify products based on their similarity to the nearest neighbor in the training data. The expected outcome of this research is to provide strong support to the store manager in making more accurate decisions regarding the best-selling products. By implementing data mining and the K-Nearest Neighbor method, this research aims to address the limitations of the current subjective analysis approach. Consequently, the company can optimize inventory management and improve operational efficiency.