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

Siti Hadijah Hasanah (Universitas Terbuka)
Eka Julianti (Universitas Terbuka)

INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
Vol 6 No 1 (2022): February 2022

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%.