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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Fersellia Fersellia
Ma’arif Nahdlatul Ulama University
Computer Science & IT

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TikTok store affiliate performance sentiment analysis using support vector machine and gradient boosting machine methods Fersellia Fersellia; Fahmi Fachri; Afdhal Fauzan; Nihayatus Zaen
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27473

Abstract

The development of social media-based e-commerce, particularly, opens new opportunities for digital affiliate systems. This study examines public perception of affiliate performance through comment sentiment analysis (positive, negative, neutral) using support vector machine (SVM) and gradient boosting machine (GBM). Data was collected from TikTok Shop comments, processed through text preprocessing, manual labeling, and then analyzed using Python. Evaluation using accuracy, precision, recall, and F1-score metrics showed that the combination of the synthetic minority oversampling technique (SMOTE) with SVM and GBM improved classification performance, although negative sentiment remained challenging. SVM achieved the highest accuracy (84%) with a ratio of 90:10, while GBM excelled in detecting neutral sentiment (F1 0.91). These findings are useful for sentiment-based marketing strategies and natural language processing (NLP) development for Indonesian-language texts on TikTok Shop.
Co-Authors Afdhal Fauzan Fahmi Fachri Nihayatus Zaen