Skripsi

KLASIFIKASI JUDUL BERITA HOAX MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE)

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Penilaian

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In recent years, the spread of fake news or hoaxes has become a serious problem that can influence public opinion. Therefore, this research aims to determine the performance of the Support Vector Machine (SVM) and Synthetic Minority Oversampling Technique (SMOTE) algorithms for classifying. The Support Vector Machine (SVM) approach was chosen because of its ability to handle classification problems on complex datasets and the SMOTE technique to handle imbalanced datasets totaling 4231 data. The highest accuracy results occurred in the SVM algorithm without SMOTE with an accuracy of 84.8%, recall of 98.7%, and f-measure of 91.5%. The influence of the SMOTE technique and the C parameter value affects the performance results of the SVM algorithm in carrying out classification. Keywords: Hoax, Support Vector Machine, dataset, Synthetic Minority Oversampling Technique, algorithm

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407004012	T149442	T1494422024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1494422024

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xv, VI-2 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

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Subject(s)

Sistem Pakar
Jurusan Teknik Informatika

Specific Detail Info

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Statement of Responsibility

UIN Farrah

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