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All Journal Sisforma: Journal of Information Systems
Shannon Dominique Saputra
Soegijapranata Catholic University
Computer Science & IT Education Engineering Library & Information Science

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Topic Analysis Using LDA-LSTM on Shopee User Comment Shannon Dominique Saputra; Albertus Dwiyoga Widiantoro Dwiyoga Widiantoro
SISFORMA Vol 13, No 1: May 2026
Publisher : Soegijapranata Catholic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/sisforma.v13i1.15534

Abstract

Shopee's growth has also shaped online shopping in Indonesia. This study analyzed user reviews to measure satisfaction and identify key service issues using a hybrid framework: LDA for topic modeling and LSTM for sentiment classification. Class imbalance was addressed using a combination of Random Oversampling and Neighborhood Cleaning Rule (ROS-NCL).The results showed that LSTM + ROS-NCL outperformed ROS, NCL, and SMOTE, with 95% accuracy and a precision, recall, and F1-score of 0.94 each. These findings demonstrate that oversampling combined with noise cleaning effectively improves performance on imbalanced data, while also providing practical insights for feature development, logistics improvements, and Shopee's promotional strategy.
Co-Authors Albertus Dwiyoga Widiantoro Dwiyoga Widiantoro