Abstract
This study examines the effectiveness of the Single Exponential Smoothing method in forecasting the number of new student admissions at the Faculty of Engineering, Christian University of Indonesia (UKI) Toraja. This study analyzes historical data on student admissions for 11 academic years, from 2013/2014 to 2023/2024, covering four study programs, namely Mechanical Engineering, Civil Engineering, Informatics Engineering, and Electrical Engineering. The research methodology includes problem identification, data collection, analysis using the Single Exponential Smoothing method, and accuracy testing with Mean Squared Error (MSE). This study tested three values of alpha (α) = 0.1, 0.5, and 0.9. The results showed that the use of alpha = 0.9 resulted in more accurate forecasting for all study programs. This level of accuracy is validated through the lowest MSE scores, namely Mechanical Engineering of 1450.61 (prediction of 159 students), Civil Engineering of 10890.23 (prediction of 147 students), Informatics Engineering of 4332.86 (prediction of 247 students), and Electrical Engineering of 674.66 (prediction of 49 students). The comparative graph analysis of the forecast results with the actual data is consistent showing that alpha = 0.9 produces the trend closest to the actual data for all study programs. The practical implications of this study include the potential to improve the accuracy of capacity planning, more efficient resource allocation, and improve the quality of engineering at the Faculty of Engineering, UKI Toraja. These results also highlight the importance of proper selection of alpha parameters in the Single Exponential Smoothing method to optimize forecasting accuracy.
Published Version
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