Perbandingan Metode Linear Regression dan Exponential Smoothing Dalam Peramalan Penerimaan Mahasiswa Baru - Fayza Apriliza; Darmansah Darmansah; Annisa Oktavyani; Dzakiyyah Al Kaazhim (JURIKOM (Jurnal Riset Komputer), 2022)
In improving the quality of higher education, the Telkom Purwokerto Institute of Technology needs to plan and make decisions about what strategies and policies can support teaching and learning activities or other activities within the institution. To plan these strategies and policies, the institution needs reference data such as data on predictions of the number of students that will be accepted in the next five years. With this predictive data, the institution can also consider various things that must be improved and plan a better marketing strategy so that new student admissions in the following year can increase. This study aims to predict the number of students who will be accepted at the Telkom Purwokerto Institute of Technology in the next five years. The overall comparison of the linear regression and exponential smoothing methods is carried out to test the accuracy of the forecasting results. The forecasting accuracy used is MAE, MSE, and MAPE. Forecasting accuracy testing was carried out using the linear regression method with the MAE, MSE, and MAPE values of 115.28 each; 15238.46; and 0.1216054793, and the exponential smoothing method with = 0.1 has the value of the results of the MAE, MSE, and MAPE forecasting accuracy tests of 327.2938; 137036,2639; and 0.2875524468. The best method for forecasting new student admissions at the Telkom Purwokerto Institute of Technology. namely the linear regression method because it has the smallest MAE, MSE, and MAPE forecasting accuracy values. The results of forecasting new student admissions at the Telkom Purwokerto Institute of Technology for the next 5 years, from 2022 to 2026, respectively, are 1369.7; 1479.4; 1589.1;1698,8; and 1808.5.