Sistem Informasi Analisa Kelulusan Mahasiswa Asia Menggunakan Exponential Smoothing Brown-Additive
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Abstract
C4.5, K-Nearest Neighbours (KNN), Naive Bayes, and SVM algorithm can be used to estimate whether a student passes or not. However, the method cannot estimate the remaining shortcomings of the credits taken by students. The author proposes using a comparison of single exponential smoothing and brown-additive exponential smoothing to analyze students’ graduation for the estimated credit they will take. Data series in the form of total credits graduated in the semester achievement index by taking into account the data confirmed students have graduated. The use of exponential smoothing with the additive tren model shows better results than the single model. The tren model was tested on 759 students who had graduated from 2010 to 2015. The best testing uses MAE occurred in the ES tren model with an alpha parameter of 0.75 and a beta parameter of 0.5 with an inaccurate difference in the estimation of the next semester's credit as many as 0,36 credits.
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