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

HAKKI, MUHAMMAD BIL and Rodiah, Desty and Kurniati, Rizki (2024) KLASIFIKASI JUDUL BERITA HOAX MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE). Undergraduate thesis, Sriwijaya University.

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Abstract

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

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Hoax, Support Vector Machine, dataset, Synthetic Minority Oversampling Technique, algorithm
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:	Muhammad Bil Hakki
Date Deposited:	05 Jul 2024 03:59
Last Modified:	05 Jul 2024 03:59
URI:	http://repository.unsri.ac.id/id/eprint/149442

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