IJTES-volume5-issue6-article1

Application of Single Exponential Smoothing in Forecasting Number of New Students Acceptance

Volume 5, Issue 6
Noveri Lysbetti Marpaung, Rahyul Amri, Edy Ervianto, Kelvin Rainey Salim

Published online:27 December 2019

Article Views: 35

Abstract

This study aims to produce the smallest Mean Absolute Percentage Error (MAPE) to show the best forecasting, the best Alpha Constant, and forecasting ability level based on forecast results. This study used Single Exponential Smoothing Method with different Alpha constants in forecasting the number of new students’ acceptance from Academic Year 1999/2000 until 2018/2019 in a private primary school, Pekanbaru, Riau Province-Indonesia. MAPE formula aims to measure the accuracy of forecast results in percentage. The analysis shows that the best forecast results in certain years are in Academic Year 2004/2005, 2010/211, and 2015/2016 with 0% MAPE. The smallest MAPE Average Value is generated by α = 0.8 with only 0.62% error, so the best forecasting used when α = 0.8. The results of MAPE average values show that all forecast results are below 10%. Therefore, all the results of the research, in general, are grouped into excellent forecasting ability. The results highlight that by using Single Exponential Smoothing Methods to accept new students in a school, relevant policies could be devised and implemented.

Reference

S. Attuluri, “Institutional changes in mexico higher education: A reform to u.s accreditations,” International Journal of Humanities, Arts and Social Sciences, vol. 5, no. 1, pp. 9–19, 2019. doi:https://dx.doi.org/10.20469/ijhss.5.10002-1
W. K. Wamaer, “Role of information technology in the world of education,” Satya Wacana Christian University, Salatiga, Indonesia, Unpublished master’s thesis, 2014.
Rahayu, “Forecasting sales at widodo makmur rice shop using the moving average method,” Nusantara University PGRI Kediri, East Java, Indonesia, Unpublished master’s thesis, 2016.
N. Rahmawati, “Sale forecasting in kawasaki motorcycle in PT.Sumber Buana motor Yogyakarta in 2013,” Universitas Negeri, Yogyakarta, Indonesia, Unpublished masters thesis, 2013.
M. Iqbal, “Linear trend analysis with least square method for forecasting student growth,” Maulana Malik Ibrahim State Islamic University, Malang, Indonesia, Tech. Rep., 2010.
S. N. Nurdela, “Application of forecasting the number of births with artificial neural network method,” The Indonesian Journal of Public Health, vol. 12, no. 2, pp. 1–8, 2017.
E. D. S. Mulyani, E. B. Sambani, and R. Cahyana, “Forecasting application of goods supplying with projection trend method and single exponential smoothing method,” National Informatics Seminar (SNIF), vol. 2, pp. 10–16, 2014.
S. Said, “Forecasting of sale volume with exponential smoothing method,” Alauddin State Islamic University, Makassar, Indonesia, Unpublished master’s thesis, 2011.
Y. Astuti, “Forecasting for black tea sales volume with exponential smoothing method at PT Perkebunan Tanbi Wonosobo,” Semarang State University, Semarang, Indonesia, Unpublished master’s thesis, 2005.
K. R. Salim and N. L. Marpaung, “Forecasting number of new students admission using exponential smoothing method,” Riau University, Riau, Indonesia, Unpublished master’s thesis, 2019.
P. Minsan, K. Jomtour, and W. Minsan, “Forecasting model for the number of long stay Japanese tourist arrivals in Chiang Mai,” Journal of Advanced Research in Social Sciences and Humanities, vol. 3, no. 4, 2018. doi: https://doi.org/10.26500/jarssh-03-2018-0405
G. Vincent, Production Planning and Inventory Control. Jakarta, Indonesia: Gramedia Pustaka Utama, 1998.
P. Fachrudin, N. Agus, and Purwanto, “Forecasting sale of car using artificial neural networks and certainty factors,” EECIS Journal, vol. 6, no. 1, pp. 3–9, 2003.
K. M. Suryaningrum and S. P. Wijaya, “Analysis and application of single exponential smoothing methodfor predicting sales in specified periods,” SNATIF Proceeding Journal, vol. 2, pp. 1–6, 2015.
P. C. Yang, Y. W. Wang, and C. H. Liu, Development of a Weighted Evolving Fuzzy Neural Network for PCB Sales Forecasting. Cambridge, MA: Elsevier Press, 2007.

To Cite this article

I. Abdennour, M. Ouardouz, A.S. Bernoussi and M. Amharref, “Energy sharing in a grid: Cellular automata approach”, International Journal of Technology and Engineering Studies, vol. 5, no. 5, pp.139–150, 2019