Penerapan Data Mining dengan Metode Single Moving Average dalam Pengolahan Data Penerimaan Siswa Baru

Abstract

The new school year student admissions can increase or decrease. This is a problem faced by MA Negeri 1 Bener Meriah in determining future strategic steps, so predictions are needed to find out the number of new students, so that all policies and decisions in preparing future plans can be fulfilled properly. The prediction process that is built will produce informative output data in the form of predicting the number of new student admissions in the coming school year period. The method used to predict the number of new admissions is the Single Moving Average method by using the Mean Square Error (MSE) and Mean Absolute Deviation (MAD) prediction accuracy to select the best model to be used in determining the prediction results. Based on the results of analysis and testing using data from the last 4 years, it was found that the number of new admissions using a 2-year moving average was 37 students with an MAD error value of 35.5 and MSE 1687.75. Meanwhile, with a 3-year moving average of 44 students with an error value accuracy of MAD 47.25 and MSE 2343.5. Therefore, the recommended prediction result is to use a 3-year moving average with the MAD approach because the resulting error value is smaller.