Text

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

---

Penilaian

---

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.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003371	T167719	T1677192025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1677192025

Publisher

: Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 78 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Teknik Informatika

Specific Detail Info

-

Statement of Responsibility

EM

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment