Predictive Trends of Major Food Prices in Indonesia: A Deep Learning Approach to Time Series Forecasting

Muhammad Ali Yafi; Mutiara Ria Despita Maharani; Nur Afra Nabilla; Amanda Sekar Adyanti

doi:10.22146/ae.104454

Predictive Trends of Major Food Prices in Indonesia: A Deep Learning Approach to Time Series Forecasting

https://doi.org/10.22146/ae.104454

Muhammad Ali Yafi^(1*), Mutiara Ria Despita Maharani⁽²⁾, Nur Afra Nabilla⁽³⁾, Amanda Sekar Adyanti⁽⁴⁾

(1) Master of Sains Agribusiness, Faculty of Economics and Management, IPB University
(2) Master of Sains Agribusiness, Faculty of Economics and Management, IPB University
(3) Master of Sains Agribusiness, Faculty of Economics and Management, IPB University
(4) Master of Agribusiness, Faculty of Agriculture, University of Jember
(*) Corresponding Author

Abstract

Price uncertainty in food commodities will have an impact on people's food consumption. Prediction of future prices is necessary to serve as a policy reference in overcoming price fluctuations. The purpose of the study is to predict the price of major agricultural food in Indonesia in 2023-2029. The research uses time series data from 1990-2022 with price variables of corn, onion red chilli, beef, and chicken. The analytical tool used to answer the research objectives is the Autoregressive Integrated Moving Average (ARIMA) model. The results of the analysis obtained the best model for predicting price forecasts, namely ARIMA on corn commodities (1,1,0), shallots (2,1,0), red chillies (1,1,0), beef (0,1,1), and chicken meat (1,1,1). The results of the prediction of the price of Indonesia's food needs in 2023-2029 as a whole tend to increase.

Keywords

ARIMA; Food; Forecasting; Price

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DOI: https://doi.org/10.22146/ae.104454