Real time prediction of four main food commodities in Indonesia and the mapping based on autoregressive integrated moving average model

Abstract

Based on the United Nations Global Pulse, food prices have a direct effect on the purchasing power of a large part of the Indonesian population. Hence, it is important to maintain stable food prices. One way to do this is by making a prediction model. Using 59 weekly rice, shallot, chicken and egg prices starting from the first week in 2018 as the cornerstone, this research uses an Autoregressive Integrated Moving Average (ARIMA) model to predict the weekly prices of these food commodities in all Indonesian provinces. This research also provides real-time prediction that can be automatically updated when any new data is inputted. Finally, this research provides a mapping visualization to make it easier for people to interpret the results. This map is equipped with a dynamic line chart to compare two provinces food trendline and shows the current price, one week and two weeks prediction. All of this is built using R 5.3.2. In this research, the error is calculated from the coefficient of variation and the result is 0.58 %, 4.1 %, 3.23 % and 2.76 % for rice, shallot, chicken, and egg weekly prices, respectively. Furthermore, this research also analyses the prediction of second-week prices of rice, shallot, chicken, and eggs in March 2019. Hopefully, this research will bring benefit to the government and farmers to make a better decision based on these predictions, so that in the future, food prices will stabilize.