SISTEM PREDIKSI KEBUTUHAN STOK BAHAN POKOK MENGGUNAKAN METODE TREND MOMENT - Koesman, Hady; Sinaga, Roi Ricardo; Hariyharan, Hariyharan; Anita, Anita (Jurnal Tekinkom (Teknik Informasi dan Komputer), 2024)
This study aims to develop a system for predicting the need for basic food stock at Mie Sop Kak Ros Restaurant using the Trend Moment method. This method was chosen because of its ability to predict stock needs based on historical sales data. The data used are monthly sales reports from January 2023 to June 2024. Through linear regression calculations, this method produces predictions that can help restaurants manage stock more efficiently, avoiding shortages or excess inventory. The calculation results show that the sales prediction for July 2024 is 650 portions, which is consistent with the previous sales increase trend. The implementation of a web-based system makes it easier to manage sales data, calculate predictions, and recommend basic food stocks. This study shows that the Trend Moment method is effective in improving the accuracy of stock forecasting and can be applied for more reliable short-term forecasting, especially if supported by regular data updates.