PERFORMANCE OF SYNTHETIC MINORITY OVER SAMPLING TECHNIQUE ON SUPPORT VECTOR MACHINE AND K NEAREST NEIGHBOR FOR SENTIMENT ANALYSIS OF METAVERSE IN INDONESIA

ANTONIO, ROY (2023) PERFORMANCE OF SYNTHETIC MINORITY OVER SAMPLING TECHNIQUE ON SUPPORT VECTOR MACHINE AND K NEAREST NEIGHBOR FOR SENTIMENT ANALYSIS OF METAVERSE IN INDONESIA. Other thesis, UNIVERSITAS KHATOLIK SOEGIJAPRANATA.

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Abstract

The metaverse is one of the most discussed things on social media, Twitter in Indonesia. This view can be both positive and negative in Indonesian society, hence the need for sentiment analysis. However, creating a sentiment classification model with unbalanced data will reduce performance. For this reason, Synthetic Minority Oversampling is needed in Support Vector Machine and K-Nearest Neighbor algorithms. The results of Synthetic Minority Oversampling can improve the accuracy of the Support Vector Machine and K-Nearest Neighbor algorithms.

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 005 Computer programming, programs & data
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: Mr Yosua Norman Rumondor
Date Deposited: 05 Oct 2023 06:20
Last Modified: 05 Oct 2023 06:20
URI: http://repository.unika.ac.id/id/eprint/32960

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