Penerapan Data Mining dengan Algoritma C4.5 dan K-nearest Neighbor untuk Prediksi Penjualan Bahan Bangunan Terlaris - Surojudin, Nurhadi; Danny, Muhtajuddin (Jurnal Informatika Ekonomi Bisnis, 2025)
The main problem faced by PT. Surya Kapuas Perkasa is the difficulty in accurately determining the types of building materials with the highest sales levels. Currently, stock determination still relies on manual estimates based on previous sales trends, which are prone to errors and inaccuracies. As a result, the company often faces the risk of overstocking products that are less in demand, or understocking products that are actually in high demand. This condition can impact the sales process, increase storage costs, and reduce customer satisfaction. To overcome this problem, a method is needed that can predict the sales of the best-selling building materials more objectively and based on historical data. This prediction will utilize sales data from the past three years by applying data mining classification techniques using the C4.5 algorithm and K-Nearest Neighbor (K-NN) through the RapidMiner application. With this approach, the company can accurately identify the types of building materials that are most in demand in the market, allowing for more precise and efficient stock management. Based on the research results, four types of building materials were found to be the best-selling out of a total of 16 types analyzed: Light Steel, Brick, Iron, and Cement, with a prediction accuracy rate of 87.16%.