<![CDATA[The rapid development of e-commerce platforms has led to an increase in the number of user reviews on various products, including skincare products. Focallure is one of the skincare brands that receives a large number of reviews on the Shopee platform, making it difficult for consumers to quickly and objectively understand product quality. Therefore, sentiment analysis is required to classify user reviews into positive, negative, and neutral categories. This study aims to analyze the quality of Focallure skincare products based on user reviews on Shopee by applying the K-Nearest Neighbor (KNN) and Naive Bayes Classifier (NBC) algorithms, as well as comparing the performance of both methods. The data were obtained through a web scraping process from the official Focalskin store on Shopee and processed through text preprocessing, term weighting using the TF-IDF method, sentiment classification, and evaluation using a Confusion Matrix. The results show that both algorithms are able to classify review sentiments effectively, with differences in accuracy, precision, recall, and F1-score values. This study is expected to help consumers understand the quality of Focallure skincare products more efficiently and provide useful insights for producers to improve product quality and marketing strategies. ]]>
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