ANALISIS SENTIMEN PENGGUNA TIK TOK SHOP MENGGUNAKAN ALGORITMA SVM

Authors

  • Qois Al Qorni Roy Universitas Bina Darma

DOI:

https://doi.org/10.31949/jensitec.v10i01.7167

Keywords:

Molting crab classification, Image Processing, Machine Learning, KNN, RFC, and SVM.

Abstract

In today's digital landscape, social media platforms like TikTok Shop have gained immense popularity as online shopping hubs. This study advocates the utilization of Support Vector Machine (SVM) methodology to assess customer sentiment concerning products available on TikTok Shop. The primary objective is to assess SVM's efficacy in categorizing customer sentiments (positive, negative) based on their responses to product reviews. The research findings revealed a classification accuracy of 78.66%, precision of 78.01%, recall of 99.09%, and an F1 score of 87.30%. While these results showcase a commendable performance, the study recognizes room for improvement. Consequently, further enhancements are recommended, particularly in refining preprocessing techniques for both data and modeling. These additional preprocessing steps are anticipated to significantly enhance sentiment classification accuracy, ensuring more reliable outcomes and providing deeper insights into customer preferences within the realm of online shopping

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Published

2023-12-08

How to Cite

Roy, Q. A. Q. (2023). ANALISIS SENTIMEN PENGGUNA TIK TOK SHOP MENGGUNAKAN ALGORITMA SVM. J-ENSITEC (Journal of Engineering and Sustainable Technology), 10(01), 870–876. https://doi.org/10.31949/jensitec.v10i01.7167