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

Article type: Research Article

Authors: Nooraeni, Rani^{a; *} | Nickelson, Jimmy^a | Rahmadian, Eko^b | Yudho, Nugroho Puspito^c

Affiliations: [a] Politeknik Statistika STIS Jakarta, Indonesia | [b] Campus Fryslan-University of Groningen, The Netherlands | [c] BPS Statistics Indonesia

Correspondence: [*] Corresponding author: Rani Nooraeni, Department of Statistical, Politeknik Statistika STIS, DKI Jakarta, 13330, Indonesia. E-mail: [email protected].

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.

Keywords: Seasonal ARIMA, AIS data, export, artificial neural network, genetic algorithm

DOI: 10.3233/SJI-210855

Journal: Statistical Journal of the IAOS, vol. 38, no. 1, pp. 277-290, 2022