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ANALISIS SENTIMEN TWITTER TERHADAP APLIKASI MYTELKOMSEL MENGGUNAKAN MODEL BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)
User satisfaction evaluation of the MyTelkomsel application is often hindered by the limited scope of conventional survey methods, which fail to capture real-time user responses. This study aims to analyze national-scale public sentiment using a Deep Learning method with Bidirectional Long Short-Term Memory (Bi-LSTM) architecture. Review data was collected via web scraping techniques using Tweet Harvest software and automatically labeled through a Transformer-based Ensemble Learning approach (IndoBERT, RoBERTa, and BERT-SMSA) to address the lack of labels in raw data. The developed classification model integrates FastText Word Embedding features to handle informal language characteristics and out-of vocabulary (OOV) words dominant in social media. Testing results using the Stratified 5-Fold Cross Validation method showed that the proposed model achieved an average accuracy of 96%, with an F1-Score of 93% on the negative class. This study concludes that the combination of Bi-LSTM architecture and automatic labeling techniques proves effective in accurately and consistently representing user sentiment as a strategic reference for service development.
Fakultas Teknik Universitas Sultan Ageng Tirtayasa
INFORMATIKA, Fakultas Teknik Universitas Sultan Ageng Tirtayasa
ANALISIS SENTIMEN TWITTER TERHADAP APLIKASI MYTELKOMSEL MENGGUNAKAN MODEL BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM) (Text)
ANALISIS SENTIMEN TWITTER TERHADAP APLIKASI MYTELKOMSEL MENGGUNAKAN MODEL BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM) (Other)
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QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)