PERAMALAN KEBUTUHAN STOK SEMBAKO MENGGUNAKAN METODE TREND MOMENT

Abstract

Forecasting stock requirements is vital for grocery businesses to optimize inventory management and meet market demand efficiently. This study addresses the challenges faced by Toko Amah, a grocery store relying on manual stock recording prone to errors, by developing a web-based forecasting system. Utilizing the Trend Moment method, the system analyzes historical sales data to predict future stock needs with greater accuracy. The study aims to enhance inventory decision-making processes, reduce human error, and improve overall business efficiency. The system was evaluated by comparing forecasted stock with actual inventory data, yielding a Mean Absolute Percentage Error (MAPE) of 20.89%. While the results indicate acceptable accuracy, further refinement is needed to minimize prediction errors. This study concludes that the Trend Moment method provides a practical solution for stock forecasting, supporting more systematic and reliable inventory management in grocery businesses.