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

Noveri Lysbetti Marpaung, Kelvin Rainey Salim, Rahyul Amri and Edy Ervianto
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Noveri Lysbetti Marpaung: Riau University, Pekanbaru, Indonesia
Kelvin Rainey Salim: Riau University, Pekanbaru, Indonesia
Rahyul Amri: Riau University, Pekanbaru, Indonesia
Edy Ervianto: Riau University, Pekanbaru, Indonesia

International Journal of Technology and Engineering Studies, 2019, vol. 5, issue 6, 169-182

Abstract: Every year, schools always accept new students. The number of accepted new students can increase every year or even decrease from the previous year. One way to find out the number of accepted new students is by doing forecasting. Using Single Exponential Smoothing Methods in acceptance of new students in a school can help that school in making policies related to it. One of Exponential Smoothing Methods that can be used in this research is Single Exponential Smoothing Method because it is more suitable for predicting things with random (irregular) fluctuations. Forecasting is an assumption towards demands that will come based on many forecasting variables, and often based on the historical timeline. This condition can be done by involving past data and placing in the future in mathematical model form. 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 using 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 one private Primary School in Pekanbaru, Riau Province-Indonesia. Alpha Constant is a weighting method applied in Exponential Smoothing Forecasting with a range of 0.1- 0.9. MAPE is the absolute average of forecast errors without regard to positive and negative signs. MAPE formula aims to measure the accuracy of forecast results in percentage. 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 research is working well as its framework.

Keywords: Forecasting system; single exponential smoothing method; alpha constant; MAPE value; forecasting ability level (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:apa:ijtess:2019:p:169-182

DOI: 10.20469/ijtes.5.10001-6

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