Evaluasi Pengaruh Parameter Alpha terhadap Akurasi Metode Single Exponential Smoothing pada Data Persediaan Barang Retail
DOI:
https://doi.org/10.70340/jirsi.v5i2.430Keywords:
Single Exponential Smoothing, parameter alpha, forecasting accuracy, inventory forecasting, time series dataAbstract
This study aims to evaluate the effect of the alpha parameter on the accuracy level of the Single Exponential Smoothing (SES) method on retail inventory data. The evaluation was conducted using the MAD, MSE, and MAPE error values. The study used Toko Murni's retail inventory data from January 2025 to February 2026, consisting of white rice, cooking oil, bread flour, and 60 ml Bango sweet soy sauce. The evaluation process was carried out using a variation of alpha values from 0.1 to 0.9. The evaluation results showed that low alpha values provide a better level of prediction accuracy than high alpha values. In the white rice data, the use of alpha 0.1 resulted in a MAD value of 75.41, MSE 7167.35, and MAPE 16.86 with a prediction result of 460.43. For cooking oil, alpha 0.1 resulted in a prediction value of 75.66 with a MAPE of 18.6, while for 60 ml Bango sweet soy sauce, it resulted in a prediction of 80.51 with a MAPE of 12.79. Meanwhile, in the bread flour data, the optimal alpha value was obtained at alpha 0.2 with a predicted result of 67.19 and MAPE of 19.71.
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D. T. Kusuma, Y. S. Purwanto, M. Y. D. Sudirman, Y. Fitriani, and A. L. Saputra, “Optimization of Alpha Parameters in Single Exponential Smoothing Method for Forecasting Coffee Raw Material Stocks,” in 2023 International Conference on Networking, Electrical Engineering, Computer Science, and Technology (IConNECT), IEEE, 2023, pp. 207–212.
E. Ostertagova and O. Ostertag, “Forecasting using simple exponential smoothing method,” Acta Electrotechnica et Informatica, vol. 12, no. 3, p. 62, 2012.
F. F. Yeng, A. Suhaimi, and S. K. Yoke, “Golden exponential smoothing: A self-adjusted method for identifying optimum alpha,” Malaysian Journal of Computing (MJoC), vol. 5, no. 2, pp. 587–596, 2020.
S. Nugus, Financial planning using Excel: forecasting, planning and budgeting techniques. Butterworth-Heinemann, 2009.
W. Setiawan, E. Juniati, and I. Farida, “The use of Triple Exponential Smoothing Method (Winter) in forecasting passenger of PT Kereta Api Indonesia with optimization alpha, beta, and gamma parameters,” in 2016 2nd International Conference on Science in Information Technology (ICSITech), IEEE, 2016, pp. 198–202.
M. H. Abdelati and H. A. Abdelwali, “Optimizing simple exponential smoothing for time series forecasting in supply chain management,” Indonesian Journal of Innovation and Applied Sciences (IJIAS), vol. 4, no. 3, pp. 247–256, 2024.
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.
H. V Ravinder, “Determining the Optimal Values of Exponential Smoothing Constants--Does Solver Really Work?.,” American journal of business education, vol. 6, no. 3, pp. 347–360, 2013.
W. Junthopas and C. Wongoutong, “Setting the initial value for single exponential smoothing and the value of the smoothing constant for forecasting using solver in microsoft excel,” Applied Sciences, vol. 13, no. 7, p. 4328, 2023.
D. Vallejo-Huanga and J. Proaño, “Performance optimization of simple exponential smoothing forecast model,” Heliyon, vol. 12, no. 1, 2026.
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.
G. Simon, “Peramalan pendaftar mahasiswa baru dengan menggunakan metode moving average, weighted moving average dan exponential smoothing,” JURNAL TEKNIK INDUSTRI, vol. 8, no. 1, pp. 13–21, 2025.
F. Furizal et al., “Understanding Time Series Forecasting: A Fundamental Study,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 7, no. 3, pp. 554–571, 2025.
A. Tawakuli, B. Havers, V. Gulisano, D. Kaiser, and T. Engel, “Survey: Time-series data preprocessing: A survey and an empirical analysis,” Journal of Engineering Research, vol. 13, no. 2, pp. 674–711, 2025.
C. Chatfield, The analysis of time series: theory and practice. Springer, 2013.
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