Prediksi Stok Barang di Toko Eko Helm Menggunakan Metode Time series Analysis

Abstrak

Eko Helm Store located in South Lampung, faces challenges in managing helmet inventory, particularly in determining the optimal stock levels for two categories: affordable and premium helmets. This study aims to forecast helmet stock requirements for the year 2024 using the ARIMA method. Weekly sales data from January to December 2024 were analyzed through stationarity testing using the Augmented Dickey-Fuller (ADF) test and differencing, followed by parameter identification based on ACF and PACF plots. The best-fitting models were identified as ARIMA(2,1,0) for premium helmets, with a Mean Squared Error (MSE) of 24.5101 and an Akaike Information Criterion (AIC) of 249.4062, and ARIMA(1,1,0) for affordable helmets, with an MSE of 32.6102 and an AIC of 250.5381. ARIMA was selected due to its ability to capture trends and seasonal fluctuations more effectively than methods such as moving average or exponential smoothing. The forecasting results estimate a stock requirement of 112 units for affordable helmets and 64 units for premium helmets over the next four weeks. The ARIMA model is integrated into an automated forecasting system that runs scheduled scripts without manual intervention. This system supports timely and precise inventory procurement decisions.