COMPARISON OF EXPONENTIAL SMOOTHING AND MULTIPLICATIVE SEASIONALITY METHODS FOR FORECASTING STUDENT GRADUATION BASED ON SEMESTER ACHIEVEMENT INDEX

Muhammad Angga Prasetyo (Universitas Prima Indonesia)
Diana Krisdianti Hutagalung (Universitas Prima Indonesia)
Donni Nasution (Universitas Prima Indonesia)

INFOKUM
Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence

Forecasting is an important tool in effective and efficient planning. This method is a continuous improvement procedure for forecasting the latest observation objects. This forecasting method focuses on the exponential decrease in priority on the object of observation that is longer. This forecasting method can only predict from data in the form of horizontal data patterns. In this study, the authors will compare the Exponential Smoothing and Winter Multiplicative Seasonality methods, where the authors will use a sample in the form of a student achievement index, so that a conclusion will be drawn from the comparison of the two methods which is better in forecasting.