Analisis Sentimen terhadap Brand Skincare Lokal Sesuai Tipe Kulit Wajah di Media Sosial X Menggunakan Algoritma Support Vector Machine dan Particle Swarm Optimization
DOI:
https://doi.org/10.55606/jutiti.v6i1.7114Keywords:
Sentiment, Skin Type, Skincare, Support Vector Machine, Particle Swarm OptimizationAbstract
Social media has transformed into one of the primary platforms for consumers to openly share their personal experiences, impressions, and opinions regarding the skincare products they use. The wide variety of reviews on platforms like X reflects genuine user responses to a product's effectiveness, which significantly depends on each individual’s specific skin type characteristics. Therefore, sentiment analysis of consumer reviews on social media has become crucial in assisting the public to identify products that are best suited to their skin conditions. This study aims to analyze sentiment toward several popular local skincare brands, specifically Wardah, Emina, and Azarine, by categorizing them based on five distinct skin types: normal, oily, combination, dry, and sensitive. Sentiment data is classified into two categories: positive and negative. For the classification process, this research employs the Support Vector Machine (SVM) algorithm as the core model. To further enhance the classification accuracy and effectiveness, the model is optimized using the Particle Swarm Optimization (PSO) algorithm to fine-tune the parameters and improve overall performance. The results of this study are expected to provide a deep evaluation of how the integrated SVM-PSO approach performs in handling review data specific to skin type categories, ultimately providing data-driven recommendations for consumers.
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