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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Indonesian Journal of Applied Statistics
Herni Utami
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science

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Journal : Indonesian Journal of Applied Statistics

 1 
Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network Herni Utami
Indonesian Journal of Applied Statistics Vol 5, No 1 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i1.56825

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 80% of Shopee application users have positive sentiments and 20% have negative sentiments, which means the data is not balance. In this study, preprocessing process with combination of synthetic minority oversampling technique (SMOTE) and Tomek link method used to handle the condition. The performance of the result is quite good, namely 80% accuracy, 84.1% precision, 92.5% sensitivity, 30% specificity, and 88.1% F1-score. It is better than performance of sentiment analysis that without preprocessing to handle imbalanced data.Keywords: sentiment analysis; imbalanced data; Tomek link; SMOTE; RNN
Co-Authors Muhammad Hisyam Lee Paulo Canas Rodrigues Subanar . Suhartono Suhartono Suhartono Suhartono Winita Sulandari Winita Sulandari