DOI: https://doi.org/10.14710/medstat.18.1.93-104

FORECAST EVALUATION OF ARIMA AND ANFIS FOR INDONESIA'S MONTHLY EXPORT (2009-2024)

*Tri Wijayanti Septiarini orcid scopus  -  Mathematics Study Program, Faculty of Science and Technology, Universitas Terbuka, Indonesia, Indonesia
Azidni Rofiqo  -  Department of Islamic Economics, Faculty of Economics and Business, Universitas Negeri Surabaya, Indonesia
Eka Pariyanti  -  Department of Doctor in Management, Graduate School, Universitas Terbuka, Indonesia
Sahidan Abdulmana  -  Department of Data Science and Analytics, Faculty of Science and Technology, Fatoni University, Thailand
Open Access Copyright (c) 2025 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Indonesia’s export sector is a key driver of economic growth, contributing significantly to foreign exchange, employment, and industrial development. Accurate forecasting of export trends is crucial for policymakers, economists, and businesses in shaping strategies and reducing risks. This study applies the Autoregressive Integrated Moving Average (ARIMA) model to forecast Indonesia’s monthly export values from January 2014 to August 2024. Dataset has been divided into training (75%) and testing (25%) subsets, and the Box-Jenkins methodology was employed, including stationarity testing, identification via ACF and PACF plots, parameter estimation, and residual diagnostics. The optimal ARIMA(1,1,1) model achieved strong predictive performance in RMSE, MSE, and MAPE. To benchmark classical methods against modern approaches, ARIMA was compared with the Adaptive Neuro-Fuzzy Inference System (ANFIS). Results indicated that ARIMA delivered higher accuracy for this dataset, reaffirming the robustness of traditional models when data characteristics align with their assumptions. It has conducted prior research evaluation via 75%:25% holdout and rolling-actual back test. This research demonstrates that classical time-series models remain highly relevant in the era of artificial intelligence, emphasizing the importance of appropriate model selection in economic forecasting.
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Keywords: Export; Forecasting; Indonesia; Time Series

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