Forecasting Rice Prices Using the ARIMA Method: A Case Study in DKI Jakarta Province

Abstract

Rice is a food structure with very important nutrients and is very much consumed by the people of Indonesia. The availability of rice in the country must be fulfilled because rice is a strategic and political food commodity. DKI Jakarta Province as the economic centre and capital city of the country has a strategic role in advancing the national economy, the movement of food commodity prices such as rice is often the centre of attention compared to other regions. Various factors can have an impact on the stability of rice prices and stocks, resulting in fluctuations in rice prices every month. For this reason, a programme is needed that can forecast the staple price of rice to illustrate the problem of food price instability in the future. In this research, we created a rice price forecasting programme using the Python programming language and Jupyter Notebook IDE by applying the ARIMA (Autoregressive Integrated Moving Average) time series method. The data used is a single data set containing rice prices in the period 2018 - 2023 with monthly granularity. The results of rice price forecasting are measured through 4 error metrics namely MSE, RMSE, MAE, and MAPE which show that the results are accurate with low error interpretation.