Comparison of Farmer Exchange Rate Index Forecasting with Decomposition and Single Exponential Smoothing Method

Isma Muthahharah; Hardianti Hafid

doi:10.30812/varian.v8i3.5491

Authors

Isma Muthahharah Universitas Negeri Makassar, Makassar, Indonesia
Hardianti Hafid Universitas Negeri Makassar, Makassar, Indonesia

DOI:

https://doi.org/10.30812/varian.v8i3.5491

Keywords:

Decomposition, Exponential Smoothing, Farmer Excahange Rate, Forecasting, Root Mean Square

Abstract

NTP forecasting is crucial for supporting appropriate policy-making. Therefore, this study aims to address the problem of selecting the most accurate forecasting method for predicting the Farmers’ Terms of Trade Index (FTTI). Specifically, the objective is to compare the accuracy of two time series forecasting methods, namely Decomposition and Single Exponential Smoothing (SES), in forecasting the price index received by food crop farmers for the period 2020 to 2024. Both methods were evaluated using Root Mean Square Error (RMSE) as a measure of forecasting accuracy. The results show that the Decomposition method provides better forecasting accuracy, as indicated by lower RMSE values (RMSE = 1.846) than the SES method, both with α = 0.1 (RMSE = 7.37) and α = 0.6 (RMSE = 3.23). This finding suggests that the Decomposition method is better at capturing seasonal patterns and trends in the FTTI data than the SES method, which tends to produce larger errors.

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References

Ahmed, Z., Kadir, A., Alam, R., Hafiz-Al-Rezoan, & Laskor, M. A. H. (2025). The impact of staple crop price instability and fragmented policy on food security and sustainable development: A case study from Bangladesh. Discover Sustainability, 6(1), 79. https://doi.org/10.1007/s43621-025-00878-7

Akbar, T., Fauzi, M., & Fajeri, H. (2019). Affecting Factors Farmer Exchange Rate (NTP) of Food Crops South Kalimantan Province. IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS), 12(7), 83–91. https://doi.org/10.9790/2380-1207018391

Aziz, A. A., Mustaffa, Z., Ismail, S., Nor, N. A. M., & Fozi, N. Q. M. (2025). A hybrid simple exponential smoothing-barnacles mating optimization approach for parameter estimation: Enhancing COVID-19 forecasting in Malaysia. MethodsX, 14, 103347. https://doi.org/10.1016/j.mex.2025.103347

Cantarella, G. E., Fiori, C., & Velona, P. (2025). Moving average vs. exponential smoothing cost-updating filters for day-to-day dynamic assignment: Fixed-point stability and bifurcation theoretical analysis. Transportation Research Part B: Methodological, 199, 103253. https://doi.org/10.1016/j.trb.2025.103253

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. https://doi.org/10.7717/peerj-cs.623

Elfira, E., Silvia, V., & Nasir, M. (2022). The Effect of Farmer’s Export, Import, and Exchange Rate on Value-Added of Agricultural Sector in Aceh Province, Indonesia. International Journal of Finance, Economics and Business, 1(2), 91–102. https://doi.org/10.56225/ijfeb.v1i2.24

Erekalo, K. T., Gemtou, M., Kornelis, M., Pedersen, S. M., Christensen, T., & Denver, S. (2025). Understanding the behavioral factors influencing farmers’ future adoption of climate-smart agriculture: A multi-group analysis. Journal of Cleaner Production, 510, 145632. https://doi.org/10.1016/j.jclepro.2025.145632

Kumar, L., Sharma, K., & Khedlekar, U. K. (2024). Dynamic pricing strategies for efficient inventory management with autocorrelative stochastic demand forecasting using exponential smoothing method. Results in Control and Optimization, 15, 100432. https://doi.org/10.1016/j.rico.2024.100432

Nusrang, M., Fahmuddin, M., Hafid, H., & Muthahharah, I. (2025). Application of ARIMA-Decomposition in Forecasting Coffee Exports in Indonesia. Inferensi, 8(1), 25. https://doi.org/10.12962/j27213862.v8i1.20899

Qian, Z., Pei, Y., Zareipour, H., & Chen, N. (2019). A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Applied Energy, 235, 939–953. https://doi.org/10.1016/j.apenergy.2018.10.080

Rozi, F., Subagio, H., Elisabeth, D. A. A., Mufidah, L., Saeri, M., Supriyadi, Burhansyah, R., Kilmanun, J. C., Krisdiana, R., Hanif, Z., Astuty, E. D., & Putri, R. L. (2025). Indonesian foodstuffs in facing global food crisis: Economic aspects of soybean farming. Journal of Agriculture and Food Research, 19, 101669. https://doi.org/10.1016/j.jafr.2025.101669

Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3, 100026. https://doi.org/10.1016/j.sca.2023.100026

Van Asseldonk, M. A., Bouwknegt, M., Wisselink, H. J., & Van Wagenberg, C. P. (2021). Quarter of pig farmers readily viewed information on Toxoplasma gondii infection in pigs provided in a data exchange system of a Dutch slaughter company. Preventive Veterinary Medicine, 193, 105410. https://doi.org/10.1016/j.prevetmed.2021.105410

Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conference Series: Materials Science and Engineering, 324, 012049. https://doi.org/10.1088/1757-899X/324/1/012049

Wasono, R., Fitri, Y., & Haris, M. A. (2024). Forecasting the Number of Airplane Passengers Using Holt-Winters Exponential Smoothing Method and Extreme Learning Machine Method. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 18(1), 0427–0436. https://doi.org/10.30598/barekengvol18iss1pp0427-0436

Wibowo, R. P., Pebriyani, D., & Indriyanti, T. (2025). Analysis of Rice Price Volatility in Medan City, Indonesia. Agro Bali : Agricultural Journal, 8(1), 291–302. https://doi.org/10.37637/ab.v8i1.2216

Zhao, H.-M., He, H.-D., Lu, K.-F., Han, X.-L., Ding, Y., & Peng, Z.-R. (2022). Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19. Transport Policy, 118, 91–100. https://doi.org/10.1016/j.tranpol.2022.01.015