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Performance Comparison between Double Exponential Smoothing and Double Moving Average Methods in Seasonal Beef Demand
Corresponding Author(s) : Bain Khusnul Khotimah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 9, No. 4, November 2024
Abstract
Beef demand relies on seasonal patterns because it depends on feed supplies, especially in the rural areas, that still rely on natural feeds. Beef supply is regulated by the government as it is one of the highly demanded commodities. It is a livestock product containing nutritional value to meet the protein needs of the community. The supply is influenced by several factors such as beef production, beef consumption, and the people's income level. In order to anticipate the increasing demand for beef, it is necessary to conduct a forecast to estimate the demand for meat in the future. In forecasting, various methods were examined to choose the method with the lowest error rate. This research compared the Mean Absolute Percentage Error (MAPE) resulted from Double Exponential Smoothing (DES) and Double Moving Average (DMA) methods. Based on the test results and analysis on beef supplies in Madura, it can be concluded that the method with the lowest MAPE value is Double Exponential Smoothing, i.e. 9.50% with an alpha parameter of 0.5. Meanwhile, the test using the Double Moving Average method to determine the best MAPE value, resulted the best time order of 2 with a MAPE value of 29.8408%. After finding the parameter with the lowest MAPE value, that parameter was used for the data testing. In the measurement, the data used for the testing were the data of 1-year, 2-year, 3-year, and 4-year period. Each method has a level of error value that increases the same; the number of data entered can affect the MAPE value. Therefore, the more data entered, the lower the error value.
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- M. Shahbandeh, “Cattle population worldwide 2012-2023,” Agriculture, Sep 19, 2023.
- K. Kozicka, J. Žukovskis, and W. Gront, E. Explaining Global Trends in Cattle Population Changes between 1961 and 2020 Directly Affecting Methane Emissions, Sustainability, Vol. 15, 10533, 2023. https://doi.org/10.3390/su151310533
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- D. Febrian, S. I. A. Idrus, and D. A. J. Nainggolan, “The Comparison of Double Moving Average and Double Exponential Smoothing Methods in Forecasting the Number of Foreign Tourists Coming to North Sumatera,” J. Phys.: Conf. Ser, Vol,1462, 2020. https://doi.org/10.1088/1742-6596/1462/1/012046
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- Q. Shao, A. Aldhafeeri, S. Qiu, and S. Khuder, “A multiplicative Holt–Winters model and autoregressive moving-average for hyponatremia mortality rates,” Healthcare Analytics, Vol. 4,100262, 2023. https://doi.org/10.1016/j.health.2023.
- R. Taboran, S. Sukparungsee, and Y. Areepong, “Mixed moving average-exponentially weighted moving average control charts for monitoring of parameter change,” in: Proceeding of the International MultiConference of Engineers and Computer Scientists, Pp.13–15. 2019.
- M. Melikoglu, and Z. K. Menekse, “Forecasting Turkey's cattle and sheep manure based biomethane potentials till 2026,” Biomass and Bioenergy, Vol.132, 105440, 2020. https://doi.org/10.1016/j.biombioe.2019.105440
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- G. A. Ryu, A. Nasridinov, H. Rah, and K. Yoo, “Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data,” Agriculture, Vol.10, No.1, 2020. https://doi.org/10.3390/agriculture10010021
- P. Guerra, Uri, L. Mamani, Natalio, P. Durand, Manuel, Pierr, Manrique, Condori, E. G. Herreros, and Manuel, “Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands,” PLoS ONE, Vol.18, 2023. https://doi./10.1371/journal.pone.0288849
- E. Kasatkina, D. Vavilova, and R. Faizullin, “Development of econometric models to forecast indicators of the livestock industry,” E3S Web of Conferences, Vol.548, 03002, 2024. https://doi.org/10.1051/e3sconf/202454803002
References
M. Shahbandeh, “Cattle population worldwide 2012-2023,” Agriculture, Sep 19, 2023.
K. Kozicka, J. Žukovskis, and W. Gront, E. Explaining Global Trends in Cattle Population Changes between 1961 and 2020 Directly Affecting Methane Emissions, Sustainability, Vol. 15, 10533, 2023. https://doi.org/10.3390/su151310533
M. Hwan Na, W. Cho, S. Kang, and Inseop Na, “Comparative Analysis of Statistical Regression Models for Prediction of Live Weight of Korean Cattle during Growth,” Agriculture, Vol.13, No.10, 1895, 27 September 2023. Comparative Analysis of Statistical Regression Models for Prediction of Live Weight of Korean Cattle during Growth
B. K. Khotimah, F. Agustina, O. R. Puspitarini, Husni, D. R. Anamisa, N. Prayugo, and A. M. S. Putri, “Random Search Hyperparameter Optimization for BPNN to Forecasting Cattle Population,” E3S Web of Conferences, Vol. 499, 01017, 2024. https://doi.org/10.1051/e3sconf/202449901017
F. Firdaus, B. A. Atmoko, E. Baliarti, T. S. M. Widi, D. Maharani, and Panjono, “The meta-analysis of beef cattle body weight prediction using body measurement approach with breed, sex, and age categories,” J Adv Vet Anim Res., Vol. 10, No. 4, Pp. 630–638, Dec. 2023. https://doi.org/10.5455/javar.2023.j718
T. C. Lwin, T. T. Zin and P. Tin, "Predicting Calving Time of Dairy Cows by Exponential Smoothing Models," 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), Kobe, Japan, Pp. 322-323, 2020. https://doi.org/10.1109/GCCE50665.2020.9291903
V. Tenrisanna, and S. N. Kasim, “Trends and forecasting of meat production and consumption inIndonesia: Livestock development strategies,” in IOP Conf. Series: Earth and Environmental Science, Vol. 492, 012156, 2020. https://doi.org/10.1088/1755-1315/492/1/012156
D. Effrosynidis, E. Spiliotis, G. Sylaios, and A. Arampatzis, “Time series and regression methods for univariate environmental forecasting: An empirical evaluation,” in Science of The Total Environment, Vol. 875, 162580, 2023. https://doi.org/10.1016/j.scitotenv.2023.162580
A. A. Dewi, and D. Idayani, “The Comparison of Simple Moving Average and Double Exponential Smoothing Methods in Predicting New Debtors,” JURTEKSI (Jurnal Teknologi dan Sistem Informasi), Vol. 9, No. 3, Pp.369-376, June 2023. DOI: https://doi.org/10.33330/jurteksi.v9i3.2254
M. A. C. Lascorz, P. J. Herrera. A. Troncoso, and G. A. Cortés, “A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting,” Information Sciences, Vol. 586, Pp. 611-627, 2022. https://doi.org/10.1016/j.scitotenv.2023.162580
I. Svetunkova. H. Chenb, and J. E. Boylan, “A new taxonomy for vector exponential smoothing and its application to seasonal time series,” European Journal of Operational Research, Vol. 304, No. 3 Pp. 964-980, 1 February 2023. https://doi.org/10.1016/j.ejor.2022.04.040
N. A. Atussaliha and P. H. Darwis, "Metode Double Exponential Smoothing pada Sistem Peramalan," ILKOM Jurnal Ilmiah, Vol. 3, Pp.183-190, Desember 2020. https://doi.org/10.33096/ilkom.v12i3.607.183-190
G. Moiseev, “Forecasting oil tanker shipping market in crisis periods: Exponential smoothing model application,” The Asian Journal of Shipping and Logistics, Vol.37, No. 3, Pp. 239-244, September 2021. https://doi.org/10.1016/j.ajsl.2021.06.002
J. F. R. Sanchez and L. M. Menezes, “Structural combination of seasonal exponential smoothing forecasts applied to load forecasting,” European Journal of Operational Research, Vol. 275, No.3, Pp. 916–924, 2019. https://doi.org/10.1016/j.ejor.2018.12.013
D. Febrian, S. I. A. Idrus, and D. A. J. Nainggolan, “The Comparison of Double Moving Average and Double Exponential Smoothing Methods in Forecasting the Number of Foreign Tourists Coming to North Sumatera,” J. Phys.: Conf. Ser, Vol,1462, 2020. https://doi.org/10.1088/1742-6596/1462/1/012046
D. Guleryuz, “Forecasting outbreak of COVID-19 in Turkey; Comparison of Box–Jenkins, Brown’s exponential smoothing and long short-term memory models,” Process Safety and Environmental Protection, Vol.149, Pp. 927-935, 2021. https://doi.org/10.1016/j.psep.2021.03.032
K. Talordphop, S. Sukparungsee, and Y. Areepong, “On designing new mixed modified exponentially weighted moving average - exponentially weighted moving average control chart,” Results in Engineering,18,101152, 2023. https://doi.org/10.1016/j.rineng.2023.
Q. Shao, A. Aldhafeeri, S. Qiu, and S. Khuder, “A multiplicative Holt–Winters model and autoregressive moving-average for hyponatremia mortality rates,” Healthcare Analytics, Vol. 4,100262, 2023. https://doi.org/10.1016/j.health.2023.
R. Taboran, S. Sukparungsee, and Y. Areepong, “Mixed moving average-exponentially weighted moving average control charts for monitoring of parameter change,” in: Proceeding of the International MultiConference of Engineers and Computer Scientists, Pp.13–15. 2019.
M. Melikoglu, and Z. K. Menekse, “Forecasting Turkey's cattle and sheep manure based biomethane potentials till 2026,” Biomass and Bioenergy, Vol.132, 105440, 2020. https://doi.org/10.1016/j.biombioe.2019.105440
M. Ordu, and Y. Zengin, “A comparative forecasting approach to forecast animal production: A case of Turkey,” Livestock Studies, Vol. 60, No.1, Pp. 25-32, 2020. https://doi.org/10.46897/lahaed.719095
S. Gokulakrishnan, G. Senthil Kumar, A. Serma Saravana Pandian, J. Ramesh, P. Thilakar, L. Radhakrishnan, and A. R. Nanthini, “Time Series Modelling and Forecasting of Prices of Cattle Feed In Tamil Nadu,” IJVASR, Vol. 53, No.2, March - April 2024.
G. A. Ryu, A. Nasridinov, H. Rah, and K. Yoo, “Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data,” Agriculture, Vol.10, No.1, 2020. https://doi.org/10.3390/agriculture10010021
P. Guerra, Uri, L. Mamani, Natalio, P. Durand, Manuel, Pierr, Manrique, Condori, E. G. Herreros, and Manuel, “Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands,” PLoS ONE, Vol.18, 2023. https://doi./10.1371/journal.pone.0288849
E. Kasatkina, D. Vavilova, and R. Faizullin, “Development of econometric models to forecast indicators of the livestock industry,” E3S Web of Conferences, Vol.548, 03002, 2024. https://doi.org/10.1051/e3sconf/202454803002