IMPROVING FORECAST ACCURACY OF INDONESIAN AGRICULTURAL EXPORTS USING ANFIS SPLITTING RATIOS - Septiarini, Tri Wijayanti; Martinasari, Made Diyah Putri (Jurnal Multidisipliner Kapalamada, 2025)
Agricultural exports are highly vulnerable to global price volatility and seasonal fluctuations, creating demand for more accurate forecasting methods. This study evaluates the Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting Indonesia’s monthly agricultural exports, addressing a gap in the literature where soft computing approaches have rarely been systematically applied. Using official trade data from 2012 to 2025, two alternative training–testing schemes (75%:25% and 80%:20%) were implemented with standard preprocessing, and forecasting accuracy was measured using RMSE, MAE, and MAPE. The results show that ANFIS delivered accuracy within widely accepted thresholds under the 75%:25% split, while accuracy declined under the 80%:20% split. Theoretically, the study contributes by clarifying conditions for reliable neuro-fuzzy forecasting and emphasizing standardized evaluation protocols. Practically, the findings provide decision-relevant insights for policymakers and exporters, supporting export target setting, forward-contract planning during volatile price swings, and logistics coordination during peak harvest seasons.