Penerapan Algoritma Single Exponential Smoothing untuk Prediksi Jumlah Calon Mahasiswa Baru

 (*)Prizhelius Anzhelmus Boli Mail (Universitas Katolik Darma Cendika, Surabaya, Indonesia)
 Ryan Putranda Kristianto (Universitas Katolik Darma Cendika, Surabaya, Indonesia)

(*) Corresponding Author

Abstract

Admission of new students is a routine agenda undertaken by a university. The number of prospective new students may increase or decrease every year at Darma Cendika Catholic University (UKDC) Surabaya. Therefore, to find out it is necessary to predict the number of prospective new students. In this case, the researcher proposes a method to predict the number of prospective new students at UKDC using the Single Exponential Smoothing method, then calculates the error using the mean square error with a time series dataset regarding applicants, prospective students and those who are accepted as new students from 2017 to 2022. This method has advantages including, namely: It has considered the influence of random, trend and seasonal on the past data to be smoothed. The results obtained by the researchers, namely the use of Single Exponential Smoothing, can be calculated in 2023 by 280 students from the prediction results.

Keywords


Prediction; University; Time Series; Single Exponential Smoothing; mean square error

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