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The Comparison of Performance Double Exponential Smoothing and Double Moving Average Methods in Seasonal Meat Stock Demand
Corresponding Author(s) : Bain Khusnul Khotimah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 9, No. 4, November 2024 (Article in Progress)
Abstract
The demand for beef supplies depended on seasonal patterns because it depends on feed supplies, especially in rural areas that still rely on natural feed. Beef supplies were part of government regulations because they were highly demanded commodities. They are livestock products that contain nutritional value to meet the protein needs of the community. Beef stocks were influenced by factors such as beef production, beef consumption, and people's income levels. In anticipating the increasing demand for beef, it is necessary to carry out forecasting to estimate the demand for meat in the future. In forecasting, various methods were used to choose the method with the lowest error rate. This research will compare the Double Exponential Smoothing (DES) with the Double Moving Average (DMA) based on the Mean Absolute Percentage Error (MAPE) measurement, one method for finding the minor error value. Based on the test results with beef supplies in Madura and conducting analysis, it can be concluded that the method with the smallest MAPE value is the Double Exponential Smoothing method, with the smallest MAPE value of 9.50% at an alpha parameter of 0.5. In testing with the Double Moving Average method, by determining the best MAPE value, the best time order in the Double Moving Average method is at time order parameter 2 with a MAPE value of 29.8408%. After finding the parameter with the smallest MAPE value, that parameter is used for data testing. In the measurement, data testing for data of 1 year, two years, three years, and four years. Each method has a level of error value that increases the same; a lot of data entered can affect the size of the MAPE value. So, the more data entered, the smaller the resulting error value.
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- M. Shahbandeh. “Cattle population worldwide 2012-2023.” Agriculture. Sep 19. 2023
- N. Widyas. S. Prastowo. T. S. M. Widi and E. Baliarti. “Predicting Madura cattle growth curve using non-linear model.” in IOP Conf. Ser.: Earth Environ. Sci. 142. 012006. 2018. DOI 10.1088/1755-1315/142/1/012006
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- Ministry of Agriculture RI. “Carcass Survey. Directorate General of Livestock and Animal Health of Indonesia. p49. 2012.
- F. Kutsiyah. Kusmartono. T. Susilawati. “Comparative study of the productivity of Madura cattle and its crossbred with Limousin in Madura Island. JITV. 8 (2). pp:98-106. 2003. DOI: 10.14334/jitv.v8i2.379
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- 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. 492. 2020. 012156. doi:10.1088/1755-1315/492/1/012156
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- A. A. Dewi. D. Idayani. “The Comparison Of Simple Moving Average And Double Exponential Smoothing Methods In Predicting New Debtors.” JURTEKSI (Jurnal Teknologi dan Sistem Informasi). 9(3). pp.369-376. June 2023. DOI: https://doi.org/10.33330/jurteksi.v9i3.2254
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- D. K. Barrow. “Forecasting intraday call arrivals using the seasonal moving average method.” Journal of Business Research. 69(12). pp. 6088–6096. 2016. doi:10.1016/j.jbusres.2016.06.016
- I. Svetunkova. H. Chenb. J. E. Boylan. A new taxonomy for vector exponential smoothing and its application to seasonal time series. European Journal of Operational Research. 304(3). Pages 964-980. 1 February 2023. https://doi.org/10.1016/j.ejor.2022.04.040
- N. A. Atussaliha. P. and H. Darwis. "Metode Double Exponential Smoothing pada Sistem Peramalan." ILKOM Jurnal Ilmiah. vol. 3. pp. 183-190. Desember 2020.
- G. Moiseev. “Forecasting oil tanker shipping market in crisis periods: Exponential smoothing model application.” The Asian Journal of Shipping and Logistics. 37(3). pp.239-244. September 2021.
- J. F. Rendon-Sanchez. L. M. Menezes. “Structural combination of seasonal exponential smoothing forecasts applied to load forecasting.” European Journal of Operational Research. 275(3). pp. 916–924. 2019.
- 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.149. pp.927-935. 2021.
- D. Febrian. S. I. A. Idrus. 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. 1462. 2020. DOI 10.1088/1742-6596/1462/1/012046
- K. Talordphop. S. Sukparungsee. Y. Areepong. “On designing new mixed modified exponentially weighted moving average - exponentially weighted moving average control chart.” Results in Engineering.18. 2023. 101152. https://doi.org/10.1016/j.rineng.2023.101152.
- Q. Shao. A. Aldhafeeri. S. Qiu. S. Khuder. “A multiplicative Holt–Winters model and autoregressive moving-average for hyponatremia mortality rates.” Healthcare Analytics.4. 2023. 100262. https://doi.org/10.1016/j.health.2023.100262.
- R. Taboran. S. Sukparungsee. 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. Z. K. Menekse. “Forecasting Turkey's cattle and sheep manure based biomethane potentials till 2026.” Biomass and Bioenergy. 132. 2020. 105440. https://doi.org/10.1016/j.biombioe.2019.105440.
- C. Chatfield. AB. Koehler. JK. Ord. “A new look at models for exponential smoothing.” J. R. Stat. Soc. Ser. D (Stat.) 50. pp.147–159. 2001.
- G. a. Ryu. A. Nasridinov. H. Rah. K. Yoo. “Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data.” Agriculture 2020. 10(1). 2020.https://doi.org/10.3390/agriculture10010021
References
M. Shahbandeh. “Cattle population worldwide 2012-2023.” Agriculture. Sep 19. 2023
N. Widyas. S. Prastowo. T. S. M. Widi and E. Baliarti. “Predicting Madura cattle growth curve using non-linear model.” in IOP Conf. Ser.: Earth Environ. Sci. 142. 012006. 2018. DOI 10.1088/1755-1315/142/1/012006
C. D Nugraha. S. Maylinda. M. Nasich. “The characteristic of Sonok and Kerapan cattle with different age at pamekasan regency Madura Island.” J Ternak Tropika. 16 (1). pp:55-60. 2015. DOI: 10.21776/ub.jtapro.2015.016.01.9
V. M. A. Nurgiartiningsih. P S Winarto. A. Susilo. A. Furqon. Genetic Evaluation of Body Weight and Body. KnE Life Sciences. September 2022.DOI:10.18502/kls.v0i0.11830
Ministry of Agriculture RI. “Carcass Survey. Directorate General of Livestock and Animal Health of Indonesia. p49. 2012.
F. Kutsiyah. Kusmartono. T. Susilawati. “Comparative study of the productivity of Madura cattle and its crossbred with Limousin in Madura Island. JITV. 8 (2). pp:98-106. 2003. DOI: 10.14334/jitv.v8i2.379
D. A. Bessler. Z. Wang. “Forecast evaluations in meat demand analysis.” Agribusiness 19(4). pp.505-523. September 2003. DOI:10.1002/agr.10074
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. 492. 2020. 012156. doi:10.1088/1755-1315/492/1/012156
D. Effrosynidis. E. Spiliotis. G. Sylaios. “A. Arampatzis. Time series and regression methods for univariate environmental forecasting: An empirical evaluation.” in Science of The Total Environment. 875. 162580. 2023. https://doi.org/10.1016/j.scitotenv.2023.162580
A. A. Dewi. D. Idayani. “The Comparison Of Simple Moving Average And Double Exponential Smoothing Methods In Predicting New Debtors.” JURTEKSI (Jurnal Teknologi dan Sistem Informasi). 9(3). pp.369-376. June 2023. DOI: https://doi.org/10.33330/jurteksi.v9i3.2254
M.A. Castán-Lascorz. P. Jiménez-Herrera. A. Troncoso. G. Asencio-Cortés. “A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting.” Information Sciences. 586. pp. 611-627. 2022. https://doi.org/10.1016/j.ins.2021.12.001.
R. J. Hyndman. A. B. Koehler. R. D. Snyder. S. Grose. “A state space framework for automatic forecasting using exponential smoothing methods.” International Journal of Forecasting.18(3). pp. 439-454. 2002. https://doi.org/10.1016/S0169-2070(01)00110-8.
D. K. Barrow. “Forecasting intraday call arrivals using the seasonal moving average method.” Journal of Business Research. 69(12). pp. 6088–6096. 2016. doi:10.1016/j.jbusres.2016.06.016
I. Svetunkova. H. Chenb. J. E. Boylan. A new taxonomy for vector exponential smoothing and its application to seasonal time series. European Journal of Operational Research. 304(3). Pages 964-980. 1 February 2023. https://doi.org/10.1016/j.ejor.2022.04.040
N. A. Atussaliha. P. and H. Darwis. "Metode Double Exponential Smoothing pada Sistem Peramalan." ILKOM Jurnal Ilmiah. vol. 3. pp. 183-190. Desember 2020.
G. Moiseev. “Forecasting oil tanker shipping market in crisis periods: Exponential smoothing model application.” The Asian Journal of Shipping and Logistics. 37(3). pp.239-244. September 2021.
J. F. Rendon-Sanchez. L. M. Menezes. “Structural combination of seasonal exponential smoothing forecasts applied to load forecasting.” European Journal of Operational Research. 275(3). pp. 916–924. 2019.
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.149. pp.927-935. 2021.
D. Febrian. S. I. A. Idrus. 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. 1462. 2020. DOI 10.1088/1742-6596/1462/1/012046
K. Talordphop. S. Sukparungsee. Y. Areepong. “On designing new mixed modified exponentially weighted moving average - exponentially weighted moving average control chart.” Results in Engineering.18. 2023. 101152. https://doi.org/10.1016/j.rineng.2023.101152.
Q. Shao. A. Aldhafeeri. S. Qiu. S. Khuder. “A multiplicative Holt–Winters model and autoregressive moving-average for hyponatremia mortality rates.” Healthcare Analytics.4. 2023. 100262. https://doi.org/10.1016/j.health.2023.100262.
R. Taboran. S. Sukparungsee. 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. Z. K. Menekse. “Forecasting Turkey's cattle and sheep manure based biomethane potentials till 2026.” Biomass and Bioenergy. 132. 2020. 105440. https://doi.org/10.1016/j.biombioe.2019.105440.
C. Chatfield. AB. Koehler. JK. Ord. “A new look at models for exponential smoothing.” J. R. Stat. Soc. Ser. D (Stat.) 50. pp.147–159. 2001.
G. a. Ryu. A. Nasridinov. H. Rah. K. Yoo. “Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data.” Agriculture 2020. 10(1). 2020.https://doi.org/10.3390/agriculture10010021