Open AccessAnalysis of CART and Random Forest on Statistics Student Status at Universitas Terbuka
Author(s) -
Siti Hadijah Hasanah,
Eka Julianti
Publication year - 2022
Publication title -
intensif
Language(s) - English
Resource type - Journals
ISSN - 2549-6824
DOI - 10.29407/intensif.v6i1.16156
Subject(s) - cart , random forest , statistics , affect (linguistics) , computer science , machine learning , artificial intelligence , mathematics education , psychology , mathematics , geography , archaeology , communication
CART and Random Forest are part of machine learning which is an essential part of the purpose of this research. CART is used to determine student status indicators, and Random Forest improves classification accuracy results. Based on the results of CART, three parameters can affect student status, namely the year of initial registration, number of rolls, and credits. Meanwhile, based on the classification accuracy results, RF can improve the accuracy performance on student status data with a difference in the percentage of CART by 1.44% in training data and testing data by 2.24%.