Food Price Prediction Using the Vector Moving Average (VMA) Model in Surabaya and Malang - Safira Najma P.; Trimono; I Gede Susrama Mas Diyasa (bit-Tech, 2025)
Price fluctuations of animal-based food are a significant issue in Indonesia, especially for low-income communities. In 2023, chicken prices increased by 4.55% and beef prices rose by 11%, contributing to inflation in East Java. Fluctuations in the prices of tuna and milkfish also affected purchasing power and the consumption of animal protein, which remains relatively low compared to other Asian countries. This study aims to predict the prices of animal-based food commodities in Surabaya City and Malang Regency using the Vector Moving Average (VMA) method, which is capable of capturing strong correlations among variables in multivariate time series data. The study covers daily prices per kilogram of beef, chicken, tuna, and milkfish throughout 2023. The 14-day price prediction at the beginning of 2024 shows that the best model for Surabaya is VMA(2), while for Malang Regency, it is VMA(3), selected based on ACF and PACF plots, low AIC values, MAPE values, and consistency of prediction results. The evaluation results using the Mean Absolute Percentage Error (MAPE) indicate that in Surabaya, beef (0.63%), chicken (2.51%), and tuna (4.76%) achieved high prediction accuracy, while milkfish (13.38%) falls into the “good” category. In Malang Regency, the VMA(3) model yielded more consistent prediction results, with all commodities showing MAPE values below 10%: beef (5.62%), chicken (2.43%), tuna (5.61%), and milkfish (2.18%). These results show that the VMA model performs well in capturing the price dynamics of food commodities, as evidenced by the low MAPE values.