ACADSTAFF UGM
| Title | : | Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network |
| Author | : |
Dr. Herni Utami, S.Si., M.Si. (1) |
| Date | : | 22 2022 |
| Keyword | : | sentiment analysis, imbalanced data, tomek link, SMOTE, RNN. sentiment analysis, imbalanced data, tomek link, SMOTE, RNN. |
| Abstract | : | Sentiment analysis on unbalanced data will cause classification errors where the classification results tend to be in the majority class. Therefore it is necessary to handle unbalanced data. In this study, a combination of synthetic minority oversampling technique (SMOTE) and tomek link methods will be used to handle unbalanced data. In this study, we use the Recurrent Neural Network (RNN) method to analyze the sentiment of shopee application users based on review data. Shopee Indonesia application review data shows that around 90% of shopee application users have positive sentiments and 10% have negative sentiments, which means the data is not balanced. The performance of the RNN model for classifying shopee application users is quite good, namely 90?curacy and 95?-score. |
| Group of Knowledge | : | Statistik |
| Original Language | : | Bahasa Indonesia |
| Level | : | Nasional |
| Status | : |
Published
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