Forecasting Model of Indonesia's Oil & Gas and Non-Oil & Gas Export Value using Var and LSTM Methods

Khaidar Ahsanur Rijal; Anik Vega Vitianingsih; Yudi Kristyawan; Anastasia Lidya Maukar; Seftin Fitri Ana Wati

doi:10.26905/jtmi.v10i1.13127

Forecasting Model of Indonesia's Oil & Gas and Non-Oil & Gas Export Value using Var and LSTM Methods

Khaidar Ahsanur Rijal, Anik Vega Vitianingsih, Yudi Kristyawan, Anastasia Lidya Maukar, Seftin Fitri Ana Wati

Abstract

As a country with abundant natural resources in the form of mineral and non-mineral products, Indonesia is characterized by its ability to fulfill domestic and foreign needs through export activities categorized into two commodities: oil and gas and non-oil and gas. Export activities are an indicator of the country's economic growth that often fluctuates in value, and these conditions are fundamentally caused by a decrease in production quantity and the instability of the global economic climate. The strategy to overcome these problems is to create a forecasting model. This research aims to develop a forecasting model using time series analysis methods, including vector autoregressive (VAR) and long short-term memory (LSTM) methods based on oil and non-oil and gas value parameters. The results of the Granger causality test stated that the values of oil and gas and non-oil and gas affect each other. The VAR model with the optimum lag produced by the Akaike Information Criterion (AIC) test obtained an accuracy value of MAPE oil & gas and non-oil and gas of 18.4% and 32.1%, respectively. LSTM generates the best model with a MAPE value of 6,23% for oil & gas and 8,18% for non-oil and gas.

Keywords

Forecasting Model; Export Value of Oil and Gas; Non-Oil and Gas Export; Deep Learning; Machine Learning; VAR method; LSTM Method.

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DOI: https://doi.org/10.26905/jtmi.v10i1.13127