Kombinasi Metode SVM Dengan Optimasi SMOTE Terhadap Ulasan Pengguna Layanan Streaming - Ni Putu Anik Juniantini; Yanti, Christina Purnama; Nilawati, Ni Ketut Utami (TEMATIK, 2024)
The development of digital technology has changed media consumption patterns in Indonesia, with streaming services like Disney+ Hotstar becoming increasingly popular. Since its launch in 2020, Disney+ Hotstar has offered exclusive content from Marvel, Pixar, Disney, National Geographic and others, quickly capturing the market in Indonesia. However, this service is not without controversy, particularly regarding certain content deemed to conflict with social values in Indonesia. Additionally, service quality, subscription prices, and content availability are also concerns for users. The difference in ratings on the Play Store (1.7) and the App Store (4.8) indicates a disparity in user satisfaction between the two platforms. This study aims to analyze user sentiment towards Disney+ Hotstar, particularly regarding reviews on the Play Store and App Store. Using a classification model with Support Vector Machine (SVM), optimized with the Synthetic Minority Over-Sampling Technique (SMOTE) to address data imbalance. Based on the analysis of 1,650 datasets, user sentiment tends to be neutral, as measured using the Vader Lexicon. The method testing results show that SMOTE optimization can improve the performance of the SVM model, with an accuracy increase of +0.7 on Play Store reviews from 0.67 to 0.74, and an accuracy increase of +0.11 on App Store reviews from 0.72 to 0.83 In conclusion, the SVM method optimized with SMOTE has proven effective in improving the accuracy of sentiment classification in user reviews of Disney+ Hotstar.