Predicting Consumer Demand At Nayla Buah Store, Ratu Agung District, Bengkulu City Using Machine Learning Technology

Abstract

The retail industry, including fruit stores, is significantly influenced by fluctuating consumer demand dynamics. An inability to accurately predict demand can lead to various operational issues for fruit stores, namely overstocking and understocking. Therefore, the ability to accurately forecast demand is crucial for business owners. The Random Forest Regressor and K-Nearest Neighbor (KNN) methods provide an evaluation of which algorithm is most suitable for predicting demand. Research results indicate that factors such as fruit type, day of the week, promotions, and weather influence the volume of consumer demand for fruit. These variables can serve as a basis for predicting fruit demand at the store. The prediction results from both models can be used to inform stock planning and operational decision-making. By forecasting daily demand more accurately, the store can mitigate the risk of losses associated with both overstocking and understocking. Overall, the Random Forest Regressor is the most recommended model for predicting fruit demand at Nayla Buah Store, as it offers higher accuracy and stability, and is capable of processing data variations more effectively.