ANALYZING THE NEW STUDENT ADMISSION FORECASTS USING SINGLE AND DOUBLE EXPONENTIAL SMOOTHING FORECASTING METHODS AT STABN SRIWIJAYA COLLEGE TANGERANG BANTEN - Pringgondani Pringgondani; Franky Okto Bernando (SEAN Institute, 2023)
This study aims to assess the utilization of Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES) techniques in predicting the number of new students at STABN Sriwijaya Tangerang. Additionally, the study aims to analyze the forecasted new student admissions for 2024 using the SES and DES methods. Lastly, the study seeks to identify the optimal value of the Constant error (α) for accurate forecasting. The research methodology employed is applied research, utilizing a population of all historical data on new student admissions. The sample for analysis spans from 2013 to 2023 and was chosen using a purposive sampling procedure. The research yielded the following results: (1) SES forecasting is conducted by determining the constants 0.1 and 0.9, followed by DES forecasting using the same constants. The forecasting methods are then evaluated using error metrics such as MAD, MSE, and MAPE. (2) Using the SES method, the forecasted number of new STABN Sriwijaya students for 2024 is 67 for an alpha value of 0.1 and 95 for an alpha value of 0.9. The Double Exponential Smoothing (DES) method predicts that there will be 80 new students in 2024 when using an alpha value of 0.1. However, using an alpha value of 0.9, the forecasted number of new students is 76. (3) The DES approach outperforms the SES method in terms of accuracy, yielding a MAPE result of 27%.