Analysis of CART and Random Forest on Statistics Student Status at Universitas Terbuka

Siti Hadijah Hasanah; Eka Julianti

doi:10.29407/intensif.v6i1.16156

Authors

Siti Hadijah Hasanah Universitas Terbuka https://orcid.org/0000-0002-5432-1403
Eka Julianti Universitas Terbuka https://orcid.org/0000-0003-2653-840X

DOI:

https://doi.org/10.29407/intensif.v6i1.16156

Keywords:

CART, Distance Learning, Ensemble, Machine Learning, Random Forest

Abstract

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%.

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