PERBANDINGAN ALGORITMA DECISION TREE(C4.5), RANDOM FOREST, DAN NAIVE BAYES UNTUK KLASIFIKASI MAHASISWA PENERIMA BEASISWA KIP UNSRI

GUSTINI, DELIA TRI and Kurniati, Rizki and Saputra, Danny Matthew (2024) PERBANDINGAN ALGORITMA DECISION TREE(C4.5), RANDOM FOREST, DAN NAIVE BAYES UNTUK KLASIFIKASI MAHASISWA PENERIMA BEASISWA KIP UNSRI. Undergraduate thesis, Sriwijaya University.

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Abstract

Kartu Indonesia Pintar (KIP) program is a government initiative to provide financial support to outstanding students who experience economic limitations. This research compares the effectiveness of three classification algorithms, namely Decision Tree (C4.5), Random Forest, and Naïve Bayes, in determining student recipients of the Kartu Indonesia Pintar (KIP) Scholarship at Sriwijaya University. The complexity in the selection of KIP recipients and the lack of an automated system are obstacles that want to be overcome. Decision Tree (C4.5) is used to form a decision tree model, while Random Forest extends the concept by utilizing a number of trees. The Naïve Bayes method, based on probability, is also used in classification. The results showed that Decision Tree (C4.5) and Random Forest had equivalent accuracy rates, reaching 94%, while Naïve Bayes achieved 93% accuracy. These findings provide a comprehensive insight into the performance of classification models for determining KIP scholarship recipients at UNSRI, and provide opportunities for the development of a more efficient and precise selection system. Keywords: Scholarships, KIP, Classification, Decision Tree(C4.5), Random Forest, Naïve Bayes

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Beasiswa, Decision Tree (C4.5), KIP, Klasifikasi, Naïve Bayes, Random Forest
Subjects:	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:	Delia Tri Gustini
Date Deposited:	31 Jan 2024 08:08
Last Modified:	31 Jan 2024 08:08
URI:	http://repository.unsri.ac.id/id/eprint/140333

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