Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Computer Science and Research
Rambe, Rezkinah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Computer Science and Research

 1 
Sentiment Analysis of Indonesian TikTok Comments Using TF‑IDF with Naive Bayes and SVM Rambe, Rezkinah; Iqbal, Muhammad
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 2 (2025): April: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to develop an automatic sentiment classification model for Indonesian TikTok comments using Term Frequency–Inverse Document Frequency (TF‑IDF) with Naive Bayes and Support Vector Machine (SVM). Fifteen thousand comments were collected from public TikTok videos and manually labeled as positive, negative, and neutral. Data preprocessing included case folding, tokenization, stopword removal, and stemming (Nazief‑Adriani algorithm). TF‑IDF weighting transformed text into vectors, then used to train both classifiers. Performance was evaluated using accuracy, precision, recall, and F1-score trough 5-fold cross-validation. Result show SVM outperforms Naive Bayes with 92.8% accuracy versus 83%. Findings confirm that TF-IDF combined with SVM produces more relieble result for short Indonesian text classification, offering valuable insights for social media monitoring applications.
Co-Authors Andysah Putera Utama Siahaan Asyifa, Nathania Datin, Maha Valne Muhammad Iqbal Rabe, Siska Mayasari Syahri, Rahma