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All Journal Applied Information Technology and Computer Science (AICOMS)
Liem, Steven
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Computer Science & IT

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Perbandingan Algoritma SVM dan Naïve Bayes Berbasis SMOTE dalam Analisis Sentimen Komentar Tiktok pada Produk Skincare Liem, Steven; Setiawan, Thomas; Pribadi , M. Rizky
Applied Information Technology and Computer Science (AICOMS) Vol 3 No 1 (2024)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v3i1.1523

Abstract

This research compares the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in sentiment analysis of TikTok comments about skincare products, using the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The evaluation results indicate that SVM outperforms Naïve Bayes, achieving an accuracy of 59.43% compared to 47.65%. Additionally, SVM excels in the F1 Score metric (60.37% versus 54.74%), although Naïve Bayes demonstrates slightly higher precision (67.96% compared to 62.76%). Therefore, SVM proves to be more effective in classifying sentiment comments, making it the recommended algorithm for sentiment analysis tasks in the skincare product domain on TikTok.
Co-Authors Pribadi , M. Rizky Setiawan, Thomas