ANALISIS SENTIMEN TIKTOK SHOP PADA TWITTER MENGGUNAKAN METODE SUPPORT VECTOR MACHINE BERBASIS PARTICLE SWARM OPTIMIZATION

ZANZABILI, MUHAMMAD REYHAN and Kurniati, Rizki and Kurniati, Junia (2025) ANALISIS SENTIMEN TIKTOK SHOP PADA TWITTER MENGGUNAKAN METODE SUPPORT VECTOR MACHINE BERBASIS PARTICLE SWARM OPTIMIZATION. Undergraduate thesis, Sriwijaya University.

Preview	Image RAMA_55201_0902128205093_cover.jpg - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (490kB) | Preview
	Text RAMA_55201_09021282025093.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_55201_09021282025093_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (7MB) | Request a copy
	Text RAMA_55201_09021282025093_0012079104_0026068907_01_frontref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB)
	Text RAMA_55201_09021282025093_0012079104_0026068907_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (580kB) | Request a copy
	Text RAMA_55201_09021282025093_0012079104_0026068907_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (368kB) | Request a copy
	Text RAMA_55201_09021282025093_0012079104_0026068907_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (771kB) | Request a copy
	Text RAMA_55201_09021282025093_0012079104_0026068907_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (307kB) | Request a copy
	Text RAMA_55201_09021282025093_0012079104_0026068907_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (311kB) | Request a copy
	Text RAMA_55201_09021282025093_0012079104_0026068907_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy

Abstract

TikTok Shop, as a social commerce feature in the TikTok application, has become a popular shopping platform in Indonesia. However, government regulations prohibiting direct transactions on social media forced TikTok to stop this service from October 4 until it reopened on December 12, 2023, after establishing a partnership with Tokopedia. This incident triggered various public opinions. This sentiment analysis research aims to analyze people's views about TikTok Shop on Twitter, then classify them into three classes, namely positive, negative and neutral. The method used is Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO). The dataset consists of 2562 data which is divided into 90% training data and 10% test data. SVM without PSO uses a linear kernel with C=1, while SVM-PSO uses an RBF kernel with parameters C=10.45 and gamma=0.309 as a result of PSO optimization. The research results show sentiment classification accuracy of 75.82% for SVM without PSO and 78.43% for SVM-PSO, with an increase in accuracy of 1.41%. This shows that PSO optimization has succeeded in increasing the effectiveness and performance of SVM in classifying public sentiment regarding TikTok Shop.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Particle Swarm Optimization, Support Vector Machine, TikTok Shop, Twitter
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Reyhan Zanzabili
Date Deposited:	05 Jun 2025 05:29
Last Modified:	05 Jun 2025 05:29
URI:	http://repository.unsri.ac.id/id/eprint/167719

Actions (login required)

View Item