Implementation of Grid Search Optimization Algorithm and Adaptive Response Rate Exponential Smoothing for Hyperparameter Tuning in Production Activity Determination - Sanjaya, Federico; Alvina, Jesslyn; Putra, Muhammad Amsar; Sitanggang, Delima (Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia, 2025)
This research aims to improve the accuracy of production planning at PT Bilah Baja Makmur Abadi by combining the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm and Grid Search optimization. The main problems faced are unpredictable demand fluctuations, dead stock risks, and high operational costs due to imbalances between production and demand. The ARRES algorithm is used for demand forecasting with adaptive exponential weighting, while Grid Search optimizes the alpha and initial year parameters to improve prediction accuracy. This study uses a 5-year sales dataset (2017-2021) with model evaluation using Mean Absolute Percentage Error (MAPE). The results showed that the combination of Grid Search and ARRES optimization algorithms proved effective in helping predict production needs. This can be seen from the significant decrease in the average MAPE value, which is 7.07% using this combination method, compared to 8.18% in the ARRSES method. The lower MAPE value indicates that the Grid Search method is effective in optimizing the ARRSES model parameters. With relatively high prediction accuracy (MAPE < 10%), this method is able to cope with unexpected demand fluctuations.