APPLICATION OF THE NEURAL NETWORK AUTOREGRESSIVE (NNAR) METHOD FOR FORECASTING THE VALUE OF OIL AND GAS EXPORTS IN INDONESIA

Abstract

Indonesia is one of the countries with the most diversity and abundant natural resources, consisting of many commodities, and has enormous trade potential with other countries The success of economic activity a country can be measured by the amount of economic growth that occurs in the country. A recession is when a country's economic condition is getting worse. Meanwhile, a recession in Indonesia is expected to occur in 2023. In a 2022 news issue written by the editorial team, tirto.id said that some experts say that if 2023 is a recession, the cause is due to a spike in inflation from the impact of the Russia-Ukraine conflict. It is known that the value of oil and gas exports affects the Indonesian economy. Any increase in the value of oil and gas exports will be followed by an increase in economic growth, and vice versa. However, over time, the value of oil and gas exports has decreased every year. Therefore, forecasting the value of oil and gas exports is needed so that the country's economic sector development strategy can be on target. In addition, oil and gas export forecasting is also needed to determine the distribution of goods exports that must be carried out. In this study, we forecast the value of oil and gas exports using the neural network autoregressive (NNAR) method. The choice of this method is made because there is no assumption of normality of the residuals and white noise like in autoregressive models. From the NNAR method, the best model results are obtained, namely NNAR (2,3) with a MAPE value of 11.75640%, which means that this model has very good forecasting performance.