Application to predict the new student’s score using time series algorithm

Abstract

With the rapid development of information technology in this era, data accuracy is essential in our daily lives to solve existing problems. The existence of information is beneficial in helping the decision-making process. Therefore, any existing information can be further processed and analyzed to be used as new knowledge so that it is useful to determine the right decision. The purpose of this research is to determine whether an application using the time series algorithm such as Auto Regression, ARMA (Auto Regression Moving Average), and Triple Exponential Smoothing model. They can forecast prediction scores that may help to solve the student's admission problem. In this case of the project, the researcher found that the Universitas Muhammadiyah Surakarta's admission system is not evaluated correctly in accepting students and controlling incoming students' quality due to the lack of insights. This time series application is one solution to help manage incoming students' quality and quantity, especially in the Universitas Muhammadiyah Surakarta. This application is developed using a web framework called Django, a full-stack Python web framework that encourages rapid growth and clean, pragmatic design. The Auto Regression model is chosen as a prediction model in One Day Service (ODS) Universitas Muhammadiyah Surakarta. It has a better performance than ARMA and Triple Exponential Smoothing and a higher chance to avoid overfitting than the other two models that are more complex for the ODS data.