Application of autoregressive integrated moving average and vector autoregression prediction models for stock and price stabilization at perum bulog - Fachrurrazi, Sayed; Ilhadi, Veri; Syukriah, Syukriah; Umaruddin, Umaruddin; Ula, Mutammimul; Zohra, Siti Fatimah A (Edelweiss Applied Science and Technology, 2024)
This study implements the ARIMA (Autoregressive Integrated Moving Average) method to predict rice stock levels at Perum Bulog in Lhokseumawe City. The objective of this research is to forecast rice stock levels for the upcoming year, 2024, and provide Perum Bulog Lhokseumawe with a more accurate reference for future stock planning. The benefit of this research lies in its potential to enable preventive measures to minimize rice stock shortages and support Perum Bulog in formulating policies to reduce the risk of future shortages. Error testing for rice prices with the ARIMA model shows MAPE values of ARIMA (1,0,0) = 21,256,076,432,276.10%, ARIMA (0,0,2) = 3.54%, and ARIMA (1,0,2) = 21,305,440,935,436.50%. Rice stock testing with MAPE error values yielded ARIMA (2,0,0) = 13.49%, ARIMA (0,0,1) = 13.65%, and ARIMA (2,0,1) = 10.27%. The error measurement results for prediction using MAPE are 1.5% for rice prices and 58.9% for rice stock. Based on these error measurements, the VAR model appears sufficiently accurate for predicting rice prices, as the MAPE value is at a low level.