Customers who buy goods will provide an assessment in the form of a review. If an item is dominated by negative reviews, other customers will be reluctant to buy at that store, so customers look for other stores and this affects the store's revenue. Therefore, the purpose of this study is to classify e-commerce beauty product reviews using the Support Vector Machine to create a model to categorize beauty product reviews and analyze accuracy. The research phase begins by collecting 50,000 datasets consisting of 35,000 training data and 15,000 test data. After the data is collected, the data labeling stage is carried out which is labeled positive and negative. Then the preprocessing step is carried out so that the data is ready to be processed in the feature extraction step. The feature extraction step aims to explore potential information that represents words. Furthermore, the resulting data is evaluated to obtain an accuracy value and determine whether the model made is feasible to use. The results showed that the Support Vector Machine can classify beauty product reviews well with an accuracy of 80.06%.
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