KLASIFIKASI UANG KULIAH TUNGGAL (UKT) DI UNIVERSITAS SRIWIJAYA MENGGUNAKAN RANDOM FOREST

ARITONANG, CAROLLINE DJULIANA and Kurniati, Rizki and Darmawahyuni, Annisa (2024) KLASIFIKASI UANG KULIAH TUNGGAL (UKT) DI UNIVERSITAS SRIWIJAYA MENGGUNAKAN RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

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Abstract

Single Tuition Fee (UKT) system in State Universities (PTN) is a response to the diversity of students' economic conditions in covering tuition fees. This research aims to determine the UKT class of students at Sriwijaya University by using the Random Forest method. This method is an ensemble method of decision trees and was chosen because of its effectiveness in capturing patterns in data and handling overfitting in unbalanced data. The target variable is the UKT class consisting of 8 categories, with a variety of testing parameters, namely n_estimator (number of trees), max_depth (tree depth), and the ratio of test data and training data. The results showed that the combination of 35 trees and max_depth 15 produced the best results with accuracy, recall, and precision of 0.89, and F1-score of 0.88, respectively. Although increasing the number of trees in the Random Forest ensemble improves accuracy, it can affect computation time. The test data and training data ratio of 20:80 resulted in an optimal accuracy of 0.89. Confusion matrix analysis shows that the model can classify UKT class labels well, but there is still difficulty in distinguishing labels that have similar features. Keywords: Random Forest, Classification, Single Tuition Fee (UKT)

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Random Forest, Uang Kuliah Tunggal (UKT)
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Carolline Djuliana Aritonang
Date Deposited:	04 Jul 2024 03:51
Last Modified:	04 Jul 2024 03:51
URI:	http://repository.unsri.ac.id/id/eprint/149179

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