Support Vector Machine for Sentiment Analysis of PT. Paragon Technology and Innovation (Case Study of Brand Make Over and Emina Product Users on Female Daily Page – Beauty Review)

JASDS: Journal of Applied Statistics and Data Science
Vol. 1 No. 1 (2024): Journal of Applied Statistics and Data Science

Support Vector Machine (SVM) is one of the classification models in Supervised Learning that is commonly used to classify user sentiment towards certain products or services. Facial care products are widely used by all circles, especially Gen-Z. The aim of this study is to obtain sentiment from the specified product reviews and apply the SVM method to predict sentiment classification. The products analyzed consisted of two Emina products, namely sunscreen and face wash and three Make Over products, namely eyebrow pencil, blush, and lipstick. The sentiment results of the five products showed that Make Over blush on products received the most positive sentiment at 98%, while Emina Sunscreen products received the least, with only at 66%. The SVM model in this study showed a good performance in making predictions with accuracy on all five products above 80%. In addition, the level of accuracy of the SVM model in classifying that the data is included in the positive class on the five products is also good because it has a recall value of > 75%. The results of this study can be used as an evaluation for PT. Paragon Technology and Innovation to continuously improve product quality and SVM models can be used to predict classification in other studies.