New recommendation to predict export value using big data and machine learning technique

Abstract

Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model.

Have Digitalization and Credit Access Accelerated Growth Performance in East Java?

“…Google trend data is still rarely used to predict macroeconomic variables such as inflation, industrial production and others. However, in line with the development of research using google trends data, several literatures/ references were found that conducted research using google trends to predict macroeconomic indicators (Nooraeni et al, 2019). Nuti et al (2014) on the use of google trends in health care research concluded that google trends can be used to study health phenomena in various topic domains.…”