Keywords
double moving average
forecasting
MAPE
single exponential smoothing
How to Cite
Abstract
Beef is considered a high-value commodity as it is an important source of protein. Interest in beef continues to rise. Beef production has risen sharply in the past decade, but declined by 7,240.68 tons in 2020 amid coronavirus lockdowns. After that, in 2021, production reached 16,381.81 tons and continued to increase in 2022 and 2023. A precise method is required to forecast beef production. One way to predict beef production in Jakarta is using the Single Exponential Smoothing and Double Moving Average methods. The two algorithms are compared to get the lowest error rate. The methodology used in this research is the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology. According to SAS Institute Inc., there are five stages in developing a system using the SEMMA methodology. After analyzing using MAPE, it is found that the algorithm with the smallest error value is the Single Exponential Smoothing algorithm with a percentage in the monthly period of 16% while for the annual period, it is 27% compared to other algorithms. The forecasting is quite accurate because the MAPE value for each algorithm used has an error of less than 31%.
References
R. Kusumaningrum, A. T. Suryana, and F. C. Hanoum, “The effect of changes in beef prices on beef supply and demand in Indonesia,” Enrichment: Journal of Management, Vol. 12, No. 1, pp. 374–384, 2021.
R. Widiyanti, N. N. Hidayat, and S. Mastuti, “Beef Marketing Channels and Efficiency in Banyumas Regency Central Java Indonesia,” Technium Sustainability, Vol. 7, pp. 43–53, 2024.
F. Achmad, “The Income Distribution and Household Food Security of Beef Cattle Farmers in The Special Region of Yogyakarta, Indonesia,” Journal of Scientific Research, Education, and Technology (JSRET), Vol. 3, No. 1, pp. 430–444, 2024.
“Tentang Jakarta.” Accessed: May 12, 2024. [Online]. Available: https://www.jakarta.go.id/tentang-jakarta
T. Rak and R. Żyła, “Using data mining techniques for detecting dependencies in the outcoming data of a Web-Based system,” Applied Sciences, Vol. 12, No. 12, p. 6115, 2022.
W. Wu, Y. Li, A. Feng, L. Li, T. Huang, A. Xu, J. Lyu, “Data mining in clinical big data: the frequently used databases, steps, and methodological models,” Mil Med Res, Vol. 8, pp. 1–12, 2021.
M. A. Jassim and S. N. Abdulwahid, “Data mining preparation: process, techniques and major issues in data analysis,” in IOP conference series: materials science and engineering, IOP Publishing, 2021, p. 012053.
J. Kaur and K. Dharni, “Application and performance of data mining techniques in stock market: A review,” Intelligent Systems in Accounting, Finance and Management, Vol. 29, No. 4, pp. 219–241, 2022.
N. Z. Salih and W. Khalaf, “Prediction of student’s performance through educational data mining techniques,” Indonesian Journal of Electrical Engineering and Computer Science, Vol. 22, No. 3, pp. 1708–1715, 2021.
Y. Zhao, C. Zhang, Y. Zhang, Z. Wang, and J. Li, “A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis,” Energy and Built Environment, Vol. 1, No. 2, pp. 149–164, 2020.
R. Gustriansyah, N. Suhandi, F. Antony, and A. Sanmorino, “Single exponential smoothing method to predict sales multiple products,” in Journal of Physics: Conference Series, IOP Publishing, 2019, p. 012036.
O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data,” in Proceedings of the European Conference of the PHM Society, 2020, pp. 1–11.
S. Prajapati, A. Swaraj, R. Lalwani, A. Narwal, and K. Verma, “Comparison of traditional and hybrid time series models for forecasting COVID-19 cases,” arXiv preprint arXiv:2105.03266, 2021.
D. M. Khairina, R. Khairunnisa, H. R. Hatta, and S. Maharani, “Comparison of the trend moment and double moving average methods for forecasting the number of dengue hemorrhagic fever patients,” Bulletin of Electrical Engineering and Informatics, Vol. 10, No. 2, pp. 978–987, 2021.
M. B. Yel, T. Tundo, and V. Arinal, “Forecasting Roof Tiles Production with Comparison of SMA and DMA Methods Based on n-th Ordo 2 and 4,” JTAM (Jurnal Teori dan Aplikasi Matematika), Vol. 8, No. 3, pp. 667–679, 2024.
S. Yondri, D. Meidelfi, T. Lestari, F. Sukma, and I. S. Mutia, “Comparative analysis of the least squares method and double moving average technique for forecasting product inventory,” Teknomekanik, Vol. 7, No. 1, pp. 74–84, 2024.
H. Hidayatulah and S. Parasian, “Comparison of forecasting accuracy rate of exponential smoothing method on admission of new students,” Journal of Critical Review, Vol. 7, No. 2, pp. 268–274, 2020.
J. N. A. Aziza, “Perbandingan Metode Moving Average, Single Exponential Smoothing, dan Double Exponential Smoothing Pada Peramalan Permintaan Tabung Gas LPG PT Petrogas Prima Services,” Jurnal Teknologi dan Manajemen Industri Terapan, Vol. 1, No. I, pp. 35–41, 2022.
M. H. W. Pranataningtyas, Y. A. Pranoto, and D. Rudhistiar, “The Vehicle Volume Forecasting System on the toll road uses the Double Moving Average and Double Exponential Smoothing Methods,” International Journal of Computer Science and Information Technology, Vol. 1, No. 1, pp. 8–14, 2024.
M. Barrios, M. Jimeno, P. Villalba, and E. Navarro, “Novel data mining methodology for healthcare applied to a new model to diagnose metabolic syndrome without a blood test,” Diagnostics, Vol. 9, No. 4, p. 192, 2019.
D. Jacob and R. Henriques, “Educational data mining to predict bachelors students’ success,” Emerging Science Journal, Vol. 7, pp. 159–171, 2023.
R. Rachmat and S. Suhartono, “Comparative analysis of single exponential smoothing and holt’s method for quality of hospital services forecasting in general hospital,” Bulletin of Computer Science and Electrical Engineering, Vol. 1, No. 2, pp. 80–86, 2020.
A. S. Ahmar, F. Fitmayanti, and R. Ruliana, “Modeling of inflation cases in South Sulawesi Province using single exponential smoothing and double exponential smoothing methods,” Qual Quant, pp. 1–11, 2021.
A. Ajiono and T. Hariguna, “Comparison of three time series forecasting methods on linear regression, exponential smoothing and weighted moving average,” International Journal of Informatics and Information Systems, Vol. 6, No. 2, pp. 89–102, 2023.
F. Sari, R. Fauziah, and H. D. EIIyany, “Prediction of Goods Stock System in Santi Electronic Shop with Double Moving Average Method,” JURTEKSI (Jurnal Teknologi dan Sistem Informasi), Vol. 8, No. 3, pp. 375–382, 2022.
D. Febrian, S. I. Al 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,” in Journal of Physics: Conference Series, IOP Publishing, 2020, p. 012046.
Y. Areepong and C. Chananet, “Double moving average control chart for zero-truncated Poisson distribution,” in Journal of Physics: Conference Series, IOP Publishing, 2021, p. 012001.
E. Vivas, H. Allende-Cid, and R. Salas, “A systematic review of statistical and machine learning methods for electrical power forecasting with reported mape score,” Entropy, Vol. 22, No. 12, p. 1412, 2020.
S. Prayudani, A. Hizriadi, Y. Y. Lase, and Y. Fatmi, “Analysis accuracy of forecasting measurement technique on random K-nearest neighbor (RKNN) using MAPE and MSE,” in Journal of Physics: Conference Series, IOP Publishing, 2019, p. 012089.
R. S. Al-Khowarizmi, M. K. M. Nasution, and M. Elveny, “Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor,” Int. J. Electr. Comput. Eng, Vol. 11, No. 3, pp. 2696–2703, 2021.
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