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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Economics, Business, & Accountancy Ventura Journal of Information Systems Engineering and Business Intelligence Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Sisfotek Global Journal of Computer System and Informatics (JoSYC) Community Development Journal: Jurnal Pengabdian Masyarakat IJPD (International Journal Of Public Devotion) Jurnal Teknologi dan Sistem Tertanam Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sisfotek Global COMMENT: Journal of Community Empowerment Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Pengabdian kepada Masyarakat (Nadimas) Jurnal Media Borneo Jurnal Informatika: Jurnal Pengembangan IT Jurnal Media Celebes Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science The Indonesian Journal of Computer Science
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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter Andi Nurkholis; Debby Alita; Aris Munandar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.324 KB) | DOI: 10.29207/resti.v6i2.3906

Abstract

PPKM is the Indonesian government's policy to deal with the spread of the coronavirus since early 2021. Until now, PPKM is still the main topic to prevent the spread of COVID-19. This policy has generated various responses from the public, especially on Twitter. A sentiment analysis process is needed to process the text obtained from Twitter. Sentiment analysis is a form of representation of text mining and text processing. This study aims to analyze public sentiment towards PPKM through data obtained from Twitter using the multi-class SVM algorithm. In implementing multi-class SVM, an analysis of the Polynomial and RBF kernels was carried out on the One Against One and One Against Rest methods which showed that the combination of One Against Rest and the Polynomial kernel was obtained the best accuracy, which was 98.9%. Unlike the case with the combination of One Against One and Kernel RBF, which obtained the worst accuracy, 77.6%. The best model produces precision, recall, and f1-score values ​​of 97%, 98%, and 97%. Based on the confusion matrix results, the best model has a positive class distribution = 912, neutral = 51, and negative = 26. Overall, the polynomial kernel model produces higher accuracy; both applied to the One Against One and One Against Rest methods. In contrast, the RBF kernel model produces lower accuracy and is significantly different when applied to the One Against One and One Against Rest methods. The model results show that public sentiment towards the PPKM policy is positive to be continued consistently to suppress the spread of the COVID-19 virus.