Penerapan Algoritma Random Forest Untuk Prediksi Penjualan Dan Sistem Persediaan Produk - Muhammad Syahrul Efendi; Sarwido; Khanif Zyen, Akhmad (Resolusi : Rekayasa Teknik Informatika dan Informasi, 2024)
This study aims to optimize sales prediction and inventory management of Bolen Crispy products in Pekalongan Village using the Random Forest algorithm. Bolen Crispy entrepreneurs face challenges in the form of large sales fluctuations and difficulties in managing inventory efficiently. Mistakes in estimating the amount of sales can lead to shortages or excess stock, which has an impact on increasing operational costs and reducing customer satisfaction. This problem of overstocking and understocking has the potential to cause financial losses. The Random Forest algorithm was chosen because of its ability to handle complex data and produce more accurate predictions. By utilizing historical sales data, this algorithm is applied to predict product demand. Testing was carried out using sales data for one year, with a division of 80% for training and 20% for testing. Initial results show that the use of the Random Forest algorithm can increase the accuracy of sales predictions by up to 85%, compared to conventional methods. With more accurate predictions, inventory management becomes more efficient, reducing the risk of shortages and excess stock.