Analysis Indonesia’s Export Value Forecasting to G20 Countries Using Long Short-Term Memory Neural Network Method


  • Veronica Universitas Prima Indonesia
  • Herlan Silaban Universitas Prima Indonesia
  • Syafrani Putri Nasution Universitas Prima Indonesia
  • Evta Indra Universitas Prima Indonesia




Forecasting, Export, G20, Long Short-Term Memory, Neural Networks


Export is one of the most important ways for the country to generate income, which can have an impact on the country's economic stability. This research aims to forecast the value of Indonesian exports to G20 member countries. The Long Short-Term Memory method is used in this research to examine historical data on Indonesian exports from the previous 16 years. Experimental results show that the LSTM Neural Network method has the ability to predict the value of Indonesian exports to G20 member countries with a sufficient level of accuracy. The predictions generated by the model provide insight into trends and fluctuations in the value of exports in the future. The results of this study provide insight into the potential application of artificial intelligence techniques in economic and trade analysis. The results demonstrate that the LSTM model is capable of producing relatively accurate predictions, with an average score of Root Mean Square Error (RMSE) on training data is 0.10 and on testing data is 0.13, as well as graphs of prediction results demonstrating that the LSTM model can capture patterns and trends from Indonesia's export data to G20 countries. According to the prediction results, the highest export value to China is expected to be $6,100,000 in the 200th month (or in the year 2039), while the lowest export value to Mexico is expected to be $27,000 in the 135th month (or in the year 2034).

GS Cited Analysis


Download data is not yet available.

Author Biography

Veronica, Universitas Prima Indonesia




Arikunto, S. (2011). Introduction to Research Methodology. Yogyakarta: SUKA-Press UIN Sunan Kalijaga. Bhardwaj, P. & Kwatra, J. (2022). Stock Market Price Forecasting Using Recurrent Neural Network.

Indonesian Journal on Computing. 7(1): 51–60.

Fadli, HF & Hidayatullah, AF (2019). Identify Cyberbullying On Social Media Using Twitter Random Forest Classification Method. Automata.

Habsy, BA (2017). The Art of Understanding Qualitative Research in Guidance and Counseling: Literature Study.

JURKAM: Andi Matappa's Counseling Journal. 1(2): 90-100.

Hidayah, N. Sulfahmi, S. Zairani, I. Yusuf, M. & Sufiati. (2019). Combine Assurance In Context Control. Scientific Journal of Economics, Management, and Accounting, 8(2): 32–37.

Husni DT (2022). Big Data Analysis of Video Games Sales Using Eda. Journal of Information and Computer Engineering. 5(1): 1-25.

Hwase, TK & Fofanah, AJ (2021). Machine Learning Model Approaches for Price Prediction in Coffee Market using Linear Regression, XGB, and LSTM Techniques. International Journal of Scientific Research in Science and Technology. 8(6): 10–48.

Ismail, Z. Yahya, A. & Shabri, A. (2019). Forecasting gold prices using multiple linear regression method.

American Journal of Applied Sciences. 6(8): 1509–1514.

Kafil, M. (2019). Application of the K-Nearest Neighbors Method. Informatics Engineering Student Journal (JATI).

(2): 59–66.

Khairu Nissa, NK Nugraha, Y. Finola, CF Ernesto, A. Kanggrawan, JI & Suherman, AL (2020). Data-Based Evaluation: Policies Restricting Public Mobility in Mitigating the Spread of COVID-19 in Jakarta. Journal of Intelligent Systems. 3(2): 84-94.

Liliana, DY Hikmah, NN & Harjono, M. (2021). Development of Indonesian Language News Sentiment Monitoring System Based on Content with Long-Short-Term Memory. Journal of Information Technology and Computer Science. 8(5): 995-1002.

Meliyana, D. & Latifah, K. (2022). Data Management and Visualization at the Pati Regency Communication and Information Service. Science And Engineering National Seminar 7 (SENS 7).

Mujtahidin, M. & Oktarianto, ML (2022). Basic Education Research Methods: Study of the Philosophy of Science Perspective. SKILLED: Journal of Basic Education and Learning. 9(1): 95–106.

Orpa, EPK, Ripanti, EF, & Tursina. (2019). Early Prediction Model of Student Study Period. System Journal And Information Technology (JUSTIN). 7(4): 272–278. (number 1)

Owen, M. Vincent, V. Br Ambarita, R. & Indra, E. (2022). Implementation of the Long Short Term Memory Method for Predicting Gold Price Movements. Journal of Information and Computer Engineering (Tekinkom), 5(1): 96-104. (number 8)

Purwaning Astuti, I. & Juniwati Ayuningtyas, F. (2018). The Effect of Exports and Imports on Economic Growth in Indonesia. Journal of Economics & Development Studies. 19(1): 1-10.

Rahmad, J. Sinurat, SH & Ryan, D. (2023). Comparison of the K-Nearest Neighbors (K-NN) and Random Forest Algorithms for Heart Failure. Informatics and Computer Technology MH.

Thamrin.9(1): 471–486.

Rizki, M. Basuki, S. & Azhar, Y. (2020). Implementation of Deep Learning Using Long Short Term Memory Architecture for Rainfall Prediction in Malang City. Repositor. 2(3): 331–338.

Tamba, SP Batubara, MD Purba, W. Sihombing, M. Mulia Siregar, VM & Banjarnahor, J. (2019). Book data grouping in libraries using the k-means clustering method. Journal of Physics: Conference Series.1230(1): 1-7.


Crossmark Updates

How to Cite

Veronica, V., Silaban, H., Nasution, S. P. ., & Indra, E. . (2023). Analysis Indonesia’s Export Value Forecasting to G20 Countries Using Long Short-Term Memory Neural Network Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 2008-2017.