<![CDATA[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.]]>
