Analisis Proyeksi Model dan Hasil Perdagangan Jagung Indonesia dengan Indikator Ratio Eskpor-Impor (RP), Indeks Spesialisasi Perdagangan (ISP) dan Relative Comparative Advantage (RCA) Pada Periode 2024-2038

Tryas Munarsyah; Mohammad Natsir; Arifin Fattah

doi:10.55606/jcsr-politama.v4i2.6054

Authors

Tryas Munarsyah Universitas Muhammadiyah Makassar
Mohammad Natsir Universitas Muhammadiyah Makassar
Arifin Fattah Universitas Muhammadiyah Makassar

DOI:

https://doi.org/10.55606/jcsr-politama.v4i2.6054

Keywords:

Competitiveness, ISP, Indonesian corn, ARIMA (1,1,0), ARIMA (1,1,2)

Abstract

This study aims to project the performance of Indonesia’s corn trade for the period 2024–2038 using the Export-Import Ratio (RP), Trade Specialization Index (ISP), and Revealed Comparative Advantage (RCA) indicators. The secondary data used cover the period 1982–2023 and are sourced from BPS, PUSDATIN, and FAO. The analytical method employs a quantitative time-series approach using Holt Exponential Smoothing and ARIMA models, evaluated based on R², RMSE, MAE, MAPE, BIC, as well as residual diagnostics (ACF and PACF) from IBM SPSS 27 output. The results show that the best model for RP is ARIMA (1,1,2) with performance values of R²=0.985; RMSE=0.071; MAE=0.042; MAPE=4.921%; and BIC=-3.922, with residuals meeting the white noise assumption (ACF ranging from -0.325 to 0.279 and PACF from -0.325 to 0.226). For ISP, the best model is ARIMA (1,1,0) with R²=0.959; RMSE=0.123; MAE=0.076; MAPE=9.891%; and BIC=-3.019, with residuals behaving randomly (ACF from -0.257 to 0.19 and PACF from -0.257 to 0.19). Meanwhile, RCA also uses ARIMA (1,1,0) with R²=0.978; RMSE=0.018; MAE=0.012; MAPE=68.076%; and BIC=-6.985, with residuals approaching white noise (ACF from -0.257 to 0.209 and PACF from -0.305 to 0.278). The selection of these models indicates that Indonesia’s corn trade performance for the period 2024–2038 is characterized by improving competitiveness without structural transformation. RP is projected to remain negative in the range of -0.899 to -0.909, confirming Indonesia’s status as a net importer. ISP shows improvement from -0.8378 to -0.7390, while RCA increases from 0.0160 to 0.0284, but remains far below the comparative advantage threshold (RCA > 1). Overall, although there is an upward trend in all trade competitiveness indicators, the improvements are gradual and not yet strong enough to transform the structure of Indonesia’s corn trade.

Downloads

Download data is not yet available.

References

Alfy, Z. R., A'ini, Z. F., & Baihaqie, A. D. (2025). Metode estimasi luas panen jagung di Provinsi Sulawesi Tenggara. Diskusi Panel Nasional Pendidikan Matematika, 11.

Badan Pusat Statistik. (2023). Statistik perdagangan luar negeri Indonesia (ekspor-impor komoditas pertanian). Badan Pusat Statistik.

Badan Pusat Statistik. (2024). Statistik Indonesia 2024. Badan Pusat Statistik.

Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2015). Time series analysis: Forecasting and control. Wiley.

Depita Kardiati. (2025). Implementasi sistem e-procurement di Dinas Perindustrian dan Perdagangan Aceh: Tantangan dan strategi optimalisasi. Perspektif Administrasi Publik dan Hukum, 2(1), 183–189. https://doi.org/10.62383/perspektif.v2i1.172

Fikri, M. N. F. R. (2022). Analisis determinan volume impor kedelai Indonesia menggunakan metode ECM (error correction model) tahun 1991–2020. Jurnal Ekonomi Bisnis, Manajemen Dan Akuntansi (JEBMA), 2(1), 18–30. https://doi.org/10.47709/jebma.v2i1.1404

Food and Agriculture Organization. (2022). FAO agricultural outlook: Market and trade developments. FAO.

Gujarati, D. N., & Porter, D. C. (2013). Dasar-dasar ekonometrika. Salemba Empat.

Hakim, D. R., Rahmiwati, A., & Flora, R. (2025). Menjelajahi dinamika pangan di era perubahan iklim terhadap dampak di Indonesia dan proyeksi masa depan: A systematic review. Ranah Research: Journal of Multidisciplinary Research and Development, 7(3), 1703–1720. https://doi.org/10.38035/rrj.v7i3.1411

Hariadi, W., Sulantari, S., & Mustika, A. (2025). Peramalan produksi gula Indonesia dengan model ARIMA (2, 2, 0) serta bagaimana strategi swasembada gula terjadi pada tahun 2030. UJMC (Unisda Journal of Mathematics and Computer Science), 11(2), 34–43.

Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10. https://doi.org/10.1016/j.ijforecast.2003.09.015

Ibnu, M. (2023). Proyeksi produksi dan keunggulan komparatif perdagangan ubi kayu Indonesia. Jurnal Ilmiah Membangun Desa dan Pertanian (JIMDP), 8(6), 249–260. https://doi.org/10.37149/jimdp.v8i6.515

Kementerian Pertanian Republik Indonesia. (2023). Outlook komoditas jagung Indonesia 2023. Pusat Data dan Sistem Informasi Pertanian.

Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and applications. Wiley.

Marwanti, M., Adi, S. H., Sosiawan, H., Sarwani, M., Irianto, G., & Wahab, M. I. (2023). Disrupsi sistem produksi padi nasional: Mampukah Indonesia memenuhi kebutuhan beras di tahun 2045? Jurnal Triton, 14(2), 403–421. https://doi.org/10.47687/jt.v14i2.588

Octavia, I. V., Lutfi, A., & Fajriyanto, F. (2025, December). Analisis prediksi produksi jagung menggunakan algoritma triple exponential smoothing untuk mendukung strategi ketahanan pangan nasional. In SINASIS (Seminar Nasional Sains), 6(1).

Rizqi, A. H., Darsono, D., & Agustono, A. (2024). Analisis trend impor gandum dan faktor yang memengaruhi impor gandum Indonesia. Agrista, 12(3).

Setiawan, W., Habibi, A., Setiawan, A. R., Nathanael, C., Silvia, N., & Wahyudi, A. (2025). Analisis proyeksi penggunaan lahan sawah untuk kebutuhan dan ketersediaan beras di Kabupaten Jember tahun 2032. Tunas Agraria, 8(2), 219–235. https://doi.org/10.31292/jta.v8i2.440

Tambunan, T. (2014). Perekonomian Indonesia: Teori dan temuan empiris. Ghalia Indonesia.

Widodo, T. (2009). Perdagangan internasional dan daya saing ekonomi Indonesia. UII Press.