Analyzing and Forecasting Admission data using Time Series Model

Problems that will be faced by higher education institutions, especially in the phase of new student admissions. Careful planning and strategies are needed in dealing with the process of admission of new students. The data for planning can be obtained using the forecasting method. The time series forecasting model is used to get forecasting data. Forecasting data is used for the decision making process. The data of new student admissions obtained is 3-period data (2017 - 2019). The data obtained is stationary. Because the data is stationary, the data does not need differentiation. The data obtained also has a sufficient correlation value, and has a loop on the 7th lag. Before making an application, a test is performed to find a time series model that is suitable for admission data. The tested models are the ARIMA model and the Autoregression model. In testing the forecast timespan, the ARIMA model gets a smaller error value in almost all tests. In the Cross-validation method, the ARIMA Model also gets a smaller RMSECV or MAECV value than the AR model. The ARIMA model was chosen to be implemented into the application. The auto_arima algorithm is used so that applications can adapt to different data. The ARIMA model is implemented into a prediction application using the Python programming language. Application development uses Django as a web-based web application framework. Bootstrap is used to create application interfaces. the result from forecasted data is acceptable for short period