Algoritma Data Mining Menggunakan Metode Decision Tree Untuk Memprediksi Pola Penjualan Produk Springbed Mengggunakan Algoritma C4.5 - Donny Sanjaya; Amir Saleh; Sri Novida Sari; Surizar Rahmi Danur (Management of Information System Journal, 2026)
Problems that often occur in the world of spring bed sales business are frequent ups and downs in predictions, the difficulty of detecting patterns in what can increase sales from buyers makes spring bed sales business people often experience losses, this also happens because business people don't know the strategy. Certainly in increasing sales, it is necessary to make predictions with a high level of accuracy, one of which is with the help of the application of computer science data mining using the C4.5 method. The C4.5 method used in this research is able to produce an optimal decision tree, with the ability to sort out the most relevant attributes in predicting springbed sales. The use of this data mining algorithm is expected to provide insight to springbed business players in making strategic decisions, such as stock management, production planning and more effective marketing campaigns. The experimental steps in this research include collecting springbed sales data. Experimental results show that the Decision Tree algorithm using the C4.5 method is able to provide spring bed sales predictions with an adequate level of accuracy. This model can help Springbed sales business players in planning more appropriate business strategies based on estimated market demand to increase the ups and downs of sales