The Model Analysis for Predictions of New Students Using Simple Linear Regression Methods Asep Jihad (a), Ida Nuraida (a), Shofira Urwatul Wutsqo (a*)
a) Universitas Islam Negeri Sunan Gunung Djati Bandung
Jalan A.H. Nasution No. 105, Cibiru, Bandung 40614
*shofiraurwatul[at]gmail.com
Abstract
The number of new students at a university for the last 3 years, starting from 2019 to 2021, UIN Sunan Gunung Djati Bandung students have increased and decreased, this is due to the COVID-19 pandemic. This data will then be used to predict the number of students in the next 3 years. The purpose of making predictions for new student admissions is to formulate the ratio of available lecturer needs to the number of new students, prepare lecture halls and other facilities. One method of making these predictions is the linear regression method. In this study, the independent variable is the period of the academic year while the dependent variable is the number of new students. The data that will be used is the data of new students of Tarbiyah and Teacher Training, Department of Mathematics and Natural Sciences Education (PMIPA) which consists of 4 study programs with MAPE (Mean Absolute Percentage Error) values, namely, Mathematics Education Study Program (8.11%) Physics Education Study Program (7.67%), Chemistry Education Study Program (7.21%) Biology Education Study Program (8.44%) Based on the results of the data analysis, a graph predicts the number of new students for the next 3 years with a linear graph pattern decreased for each study program.
Keywords: Linear Regression, New Student, Mean Absolute Percentage Error, Prediction
