Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Indonesia Sosial Teknologi
Lestari, Lutfiana Sinta
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Sentiment Analysis on Skincare Product Reviews Using Lexicon-Based and Comparison of SVM Kernel Lestari, Lutfiana Sinta; Sutrisno, Tri; Lewenusa, Irvan
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 11 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i11.8769

Abstract

The skincare industry has seen remarkable growth in recent years, fueled by increasing public awareness of skincare and beauty. As awareness of the importance of skincare grows, skincare products are becoming more popular. The skincare brands available on the market today are diverse. However, not all skincare products offer the same quality, and some are more suitable for specific skin types or concerns, depending on the ingredients used and product formulation. To help consumers understand skincare reviews, this study conducts sentiment analysis on skincare products, identifying whether reviews tend to be positive, negative, or neutral. The sentiment analysis utilizes a lexicon-based approach with comparisons of various SVM kernels, including linear, polynomial, RBF, and sigmoid. Additionally, this research applies the Term Frequency-Inverse Document Frequency (TF-IDF) for word weighting. The study results indicate that the best performance was achieved with the Sigmoid and Linear kernels when no oversampling technique was applied. The results for the linear kernel without balancing achieved 81.83% accuracy, 77.46% precision, 81.83% recall, and 79.53% F1 score. Meanwhile, the Sigmoid kernel yielded 81.83% accuracy, 77.39% precision, 81.83% recall, and 79.53% F1-score.
Co-Authors Irvan Lewenusa, Irvan TRI SUTRISNO