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PERBANDINGAN ALGORITMA K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE DALAM MEMPREDIKSI KELULUSAN MAHASISWA

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Penilaian

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Students are an important asset for any educational institution. Therefore, student graduation rates need to be noticed and considered. The percentage of on-time graduation of students is one of the points of accreditation assessment in higher education. With this, action is needed from the institution in the form of monitoring and evaluating student graduation. One way that can be done is by using classification to predict student graduation. In this study, the performance of the K-Nearest Neighbor and Support Vector Machine algorithms will be compared in predicting student graduation. For K-NN, tried 15 values of k ranging from 1 to 15. The results showed that the highest accuracy was obtained by the value of k = 4 with an accuracy of 82%. For SVM, using the linear method obtained an accuracy of 77%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207003727	T79012	T790122022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T790122022

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xx, 126 hlm.: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

003.107

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Teknik Informatika
Identifikasi istem

Specific Detail Info

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Statement of Responsibility

SEPTA

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