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FUZZY VECTOR AUTOREGRESSION FOR FORECASTING FARMER EXCHANGE RATE IN CENTRAL JAVA PROVINCE

*Nurhayadi Nurhayadi orcid scopus  -  Department of Mathematics and Science Education, Tadulako University, Indonesia
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Computer technology has developed to a very advanced measure. Calculations using complex formulas are no longer an obstacle for industry and researchers. Along with advances in computing technology, the development of fuzzy system models is also experiencing rapid progress. This paper proposes a fuzzy model combined with Vector Autoregression. The fuzzy membership function is built by selecting the median of each set to be the center of the fuzzy set. The function chosen as the membership function is Gaussian. The fuzzy Vector Autoregression model obtained was applied to the Farmer's Exchange Rate in Central Java Province. The accuracy of the model is measured based on the Mean Absolute Percentage Error. The results of model trials on FER Central Java in 2014-2020, show a pretty good forecast, namely forecasting with MAPE around 5%, and not exceeding 10%.
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Keywords: Vector; Autoregression; Fuzzy; Gaussian; Median

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  1. Abadi, A.M., Nurhayadi, Musthofa, 2017. Optimization of Wavelet Weighted Fuzzy Model for Time Series Data and its Application to Forecast Jakarta Composite Index. Journal of Engineering and Applied Sciences, 12, 5672–5678
  2. Abadi, A.M., Wustqa, D.U., Nurhayadi, 2019. Diagnosis of Brain Cancer Using Radial Basis Function Neural Network with Singular Value Decomposition Method. International Journal of Machine Learning and Computing, 9, 527–532. https://doi.org/10.18178/ijmlc.2019.9.4.836
  3. Adhi, W, 2022, Jawa Tengah Province in Figures, BPS-Statistics of Jawa Tengah Province, Semarang
  4. Chilwal, B., Mishra, P.K., 2020. A Survey of Fuzzy Logic Inference System and Other Computing Techniques for Agricultural Diseases, in: Singh Tomar, G., Chaudhari, N.S., Barbosa, J.L.V., Aghwariya, M.K. (Eds.), International Conference on Intelligent Computing and Smart Communication 2019, Algorithms for Intelligent Systems. Springer Singapore, Singapore, 1–6. https://doi.org/10.1007/978-981-15-0633-8_1
  5. Cordón, O., Herrera, F., Jesus, M.J., Villar, P., Zwir, I., 2000. Different Proposals to Improve the Accuracy of Fuzzy Linguistic Modeling, in: Ruan, D., Kerre, E.E. (Eds.), Fuzzy If-Then Rules in Computational Intelligence. Springer US, Boston, MA, 189–221. https://doi.org/10.1007/978-1-4615-4513-2_9
  6. Dahiri, D., 2018. Upaya Meningkatkan Kesejahteraan Petani Tanaman Pangan. Buletin APBN 3
  7. Han, H., Xiong, J., Zhao, K., 2021. Digital Inclusion in Social Media Marketing Adoption: The Role of Product Suitability in the Agriculture Sector. Inf Syst E-Bus Manage. https://doi.org/10.1007/s10257-021-00522-7
  8. Murdianto, E., 2020. Sosiologi Pedesaan: Pengantar Untuk Memahami Masyarakat Desa, Revisi. ed. UPN ”Veteran” Yogyakarta Press, Yogyakarta
  9. Nurhayadi, Subanar, Abdurakhman, Abadi, A.M., 2014. Fuzzy Model Translation for Time Series Data in the Extent of Median Error and its Application. Applied Mathematical Sciences, 8(43), 2113–2124. http://dx.doi.org/10.12988/ ams.2014.42114
  10. Nurhayadi, Subanar, Abdurakhman, Abadi, A.M., Hidayatullah, R., Rizal, M., Sudarman, 2020. Fuzzy Model Optimization using of Giving the Amplitude Scale Factor. Systematic Reviews in Pharmacy, 11, 666–670
  11. Reddy SK, B.A., 2015. Exchange Rate Forecasting using ARIMA, Neural Network and Fuzzy Neuron. J Stock Forex Trad 04. https://doi.org/10.4172/2168-9458.1000155
  12. Rosmayanti, R., 2019. Kementan: Tren Ekspor Produk Perkebunan Indonesia Meningkat. Warta Ekonomi
  13. Sagheer, A., Kotb, M., 2019. Time Series Forecasting of Petroleum Production Using Deep LSTM Recurrent Networks. Neurocomputing, 323, 203–213. https://doi.org/ 10.1016/j.neucom.2018.09.082
  14. Torbat, S., Khashei, M., Bijari, M., 2018. A Hybrid Probabilistic Fuzzy ARIMA Model for Consumption Forecasting in Commodity Markets. Economic Analysis and Policy, 58, 22–31. https://doi.org/10.1016/j.eap.2017.12.003
  15. Trimono, T., Sonhaji, A., Mukhaiyar, U., 2020. Forecasting Farmer Exchange Rate in Central Java Province using Vector Integrated Moving Average. Media Statistika, 13(2), 182–193. https://doi.org/10.14710/medstat.13.2.182-193
  16. Xie, Y., Zhang, P., Chen, Y., 2021. A Fuzzy ARIMA Correction Model for Transport Volume Forecast. Mathematical Problems in Engineering, 1–10. https://doi.org/ 10.1155/2021/6655102

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