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Rahmawati, Ira Tri
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Computer Science & IT

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Journal : Building of Informatics, Technology and Science

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Perbandingan Algoritma SVM, Random Forest, KNN untuk Analisis Sentimen Terhadap Overclaim Skincare pada Media Sosial X Rahmawati, Ira Tri; Alita, Debby
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6782

Abstract

The cosmetic industry in Indonesia, especially skincare products, is growing rapidly along with changes in people's lifestyles and technological advances. One of the main issues that arise is overclaiming, which can harm consumers and damage the company's reputation. This study aims to compare the performance of three algorithms in sentiment analysis of skincare overclaims on X social media. The evaluated algorithms include Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN). The research dataset consists of 7,774 tweets collected between October 1 and November 30, 2024, with 5,559 tweets after the preprocessing stage, consisting of 4,281 negative sentiment tweets and 1,275 positive sentiment tweets. Data imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), with 80% data split for training and 20% for testing. The results showed that before the application of SMOTE, the Random Forest algorithm had the highest accuracy of 95%, followed by Support Vector Machine at 91% and K-Nearest Neighbors at 80%. After the application of SMOTE, the accuracy increased significantly, with Random Forest reaching 98%, Support Vector Machine 97%, and K-Nearest Neighbors 84%. Random Forest proved to be the best algorithm, with the highest performance before and after SMOTE implementation, and was effective in handling both sentiment classes. This research provides insights for the skincare industry and regulators to detect and address product over-claiming issues through machine learning-based approaches.
Co-Authors Alita, Debby