Prediksi Penerimaan Siswa Baru dengan Metode Single Exponential Smoothing Melalui Metrik Evaluasi MAD, MSE dan MAPE
-
Diki Arisandi
*
Universitas Abdurrab, Pekanbaru,
Indonesia
- Salamun Salamun Universitas Abdurrab, Pekanbaru, Indonesia
- Aldi Ramadhan Putra Universitas Abdurrab, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Finding a solution to the issue of predicting the quantity of new student admissions is the goal of this study. Forecasting new student admissions is crucial for an educational institution to maintain sustainability and success. At the moment, sustainability efforts in educational institutions are mostly dependent on the activity of forecasting new student intakes. The single exponential smoothing method is one strategy used in the new student admissions process, particularly at PKBM Tahfidz At Tamam, to forecast the number of students whom would be admitted in the following year. A Pekanbaru private school named PKBM Tahfidz At Tamam places a strong emphasis on the memorizing of the Qur'an by its students in order to create a generation that is knowledgeable about both the Qur'an and the Sunnah. In order to make predictions based on historical data, the applied method makes use of statistical techniques that involve mathematical equations. The number of students who will be admitted in the following year is predicted using information on new student admissions from prior years. The error margin is calculated using a number of metrics, including MAD (Mean Absolute Deviation), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error). According to the study's findings, the single exponential smoothing method yields reliable forecasts for PKBM Tahfidz At Tamam's anticipated intake of new students the next year. Using this approach, the institution may better decide how to allocate resources, establish the curriculum, and maintain the facilities to meet the demands of potential students. As a result, the single exponential smoothing is critical to projecting the number of prospective students enrollments.
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