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Submitted
January 8, 2026
Accepted
February 25, 2026
Published
April 30, 2026
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Abstract

Indonesia, as an agrarian country, places the agricultural sector as a vital pillar of its economy and food security, with farmers’ welfare measured through the Farmers’ Terms of Trade (FTT). This study aims to compare the performance of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models in forecasting FTT using monthly data from 2009 to 2024 obtained from BPS (Statistics Indonesia). The results show that the SARIMA(0,1,1)(0,1,1)₁₂ model demonstrates higher accuracy with a MAPE value of 5.29%, compared to SARFIMA(1,0.2688,0)(0,1,1)₁₂ with a MAPE of 5.97%. However, the relatively small difference in MAPE indicates the presence of long-memory characteristics in the FTT data, although it does not significantly improve forecasting accuracy. The forecast results based on the best SARIMA model predict that FTT will gradually increase throughout 2025, peaking at 127.2920 in December, with a temporary decline from March to May. These findings can serve as a basis for the government to formulate targeted agricultural policies, price control measures, subsidy distribution, and marketing strategies that enhance farmers’ welfare and support national food security.

Keywords

Farmers’ Terms of Trade SARIMA SARFIMA Forecasting Long Memory

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Copyright (c) 2026 Miftah Rizky Viranty, Yeni Rahkmawati, Al Hujjah Asianingrum

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How to Cite
Viranty, M. R. ., Rahkmawati, Y., & Asianingrum, A. H. (2026). Long-Memory Modeling of Farmers’ Terms of Trade in Indonesia: A Comparative Analysis of SARIMA and SARFIMA Approaches. EKSAKTA: Journal of Sciences and Data Analysis, 7(1). https://doi.org/10.20885/EKSAKTA.vol7.iss1.art6
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