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Predicting Indonesia's Manufacturing Exports in the RCEP Region: A Machine Learning Approach to the Gravity Model of Trade

Ilham Febri Budiman, Nindyta Lutfia Annur, Rachel Jenny Monintja

View Author Affiliations
  • Ilham Febri Budiman: Politeknik Keuangan Negara STAN, Indonesia
  • Nindyta Lutfia Annur: Politeknik Keuangan Negara STAN, Indonesia
  • Rachel Jenny Monintja: Politeknik Keuangan Negara STAN, Indonesia
DOI:
https://doi.org/10.59431/ijer.v6i2.763
Published:
June 28, 2026
Pages:
311–327

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Abstract

This study evaluates the structural determinants of Indonesia's manufacturing exports to Regional Comprehensive Economic Partnership (RCEP) member countries from 2007 to 2024. By utilizing a machine learning approach through the Random Forest algorithm within the Knowledge Discovery in Databases framework, this research transcends the rigid linearity assumptions of traditional econometric models. The computational model demonstrates superior predictive precision, with the model’s accuracy reaching a score of 0.983. A specialized variable importance analysis reveals that economic similarity is the primary catalyst for export flows, thereby strongly confirming the Linder hypothesis regarding intra-industry trade. Furthermore, fundamental gravity model variables, namely economic distance, population size, and gross domestic product, remain highly significant in dictating bilateral trade volumes. Interestingly, the empirical results indicate that the implementation of the RCEP pact and the global pandemic shock possess negligible immediate impacts on the export volume. This anomaly suggests a substantial time lag effect, as domestic industrial supply chains require a considerably long adaptation period to optimally capitalize on tariff eliminations. Consequently, this paper recommends strategic policy interventions for fiscal and customs authorities. The government must synergize logistical infrastructure improvements with targeted industrial incentives, particularly the super tax deduction for research and development and the Import Facility for Export Purposes. These synchronized efforts are essential to reduce economic distance friction, enhance structural competitiveness, and transform Indonesia's participation in the RCEP agreement into tangible manufacturing trade creation.

Keywords

Economic Distance; Gravity Model; Indonesia; Machine Learning; Manufacturing Exports Random Forest
Author Biographies
Ilham Febri Budiman

Politeknik Keuangan Negara STAN

Google Scholar ResearchGate ORCID

Program Studi State Financial Management, Politeknik Keuangan Negara STAN, South Tangerang City, Banten Province, Indonesia.

Nindyta Lutfia Annur

Politeknik Keuangan Negara STAN

Google Scholar ResearchGate ORCID

Program Studi State Financial Management, Politeknik Keuangan Negara STAN, South Tangerang City, Banten Province, Indonesia.

Rachel Jenny Monintja

Politeknik Keuangan Negara STAN

Google Scholar ResearchGate ORCID

Program Studi State Financial Management, Politeknik Keuangan Negara STAN, South Tangerang City, Banten Province, Indonesia.

Article Identifiers
  • Article Title: Predicting Indonesia's Manufacturing Exports in the RCEP Region: A Machine Learning Approach to the Gravity Model of Trade
  • DOI: 10.59431/ijer.v6i2.763
  • Publication Date: 2026-06-28
  • Journal: Indonesian Journal Economic Review (IJER)
  • Volume: 6
  • Issue: 2
  • Pages: 311–327
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Issue Information

Article Details

Volume: 6
Issue: 2
Year: 2026
Published: 2026-06-28
Pages: 311–327
Section: Articles
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Budiman, I. F., Annur, N. L., & Monintja, R. J. (2026). Predicting Indonesia’s Manufacturing Exports in the RCEP Region: A Machine Learning Approach to the Gravity Model of Trade. Indonesian Journal Economic Review (IJER), 6(2), 311–327. https://doi.org/10.59431/ijer.v6i2.763
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