TikTok store affiliate performance sentiment analysis using support vector machine and gradient boosting machine methods
- DOI
- 10.12928/telkomnika.v24i3.27473
- Published
- 2026-06-01
- Container
- TELKOMNIKA (Telecommunication Computing Electronics and Control)
- Publisher
- Universitas Ahmad Dahlan
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.12928/telkomnika.v24i3.27473,
title = {TikTok store affiliate performance sentiment analysis using support vector machine and gradient boosting machine methods},
author = {Fersellia Fersellia and Fahmi Fachri and Afdhal Fauzan and Nihayatus Zaen},
year = {2026},
journal = {TELKOMNIKA (Telecommunication Computing Electronics and Control)},
doi = {10.12928/telkomnika.v24i3.27473},
url = {https://doi.org/10.12928/telkomnika.v24i3.27473}
}RIS
TY - JOUR TI - TikTok store affiliate performance sentiment analysis using support vector machine and gradient boosting machine methods AU - Fersellia Fersellia AU - Fahmi Fachri AU - Afdhal Fauzan AU - Nihayatus Zaen PY - 2026 JO - TELKOMNIKA (Telecommunication Computing Electronics and Control) DO - 10.12928/telkomnika.v24i3.27473 UR - https://doi.org/10.12928/telkomnika.v24i3.27473 ER -
APA
Fersellia, F., Fachri, F., Fauzan, A., & Zaen, N. (2026). TikTok store affiliate performance sentiment analysis using support vector machine and gradient boosting machine methods. TELKOMNIKA (Telecommunication Computing Electronics and Control). https://doi.org/10.12928/telkomnika.v24i3.27473
Source records
- crossref · retrieved 2026-09-26T06:32:00.640Z