Skripsi

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

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Penilaian

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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

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407001427	T140333	T1403332023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1403332023

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xvi, 102 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.360 7

Content Type

Text

Media Type

unmediated

Carrier Type

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Edition

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Subject(s)

Prodi Teknik Informatika
Program Aplikasi dengan Kegunaan Khusus

Specific Detail Info

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Statement of Responsibility

SEPTA

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