JULKIFLI, JULKIFLI (2026) ANALISIS SENTIMEN TWITTER TERHADAP APLIKASI MYTELKOMSEL MENGGUNAKAN MODEL BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM). S1 thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa.
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Abstract
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.
| Item Type: | Thesis (S1) | |||||||||
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| Additional Information: | Evaluasi kepuasan pengguna terhadap aplikasi MyTelkomsel seringkali terkendala oleh keterbatasan jangkauan metode survei konvensional yang tidak mampu menangkap respons pengguna secara waktu nyata. Penelitian ini bertujuan untuk menganalisis sentimen publik berskala nasional menggunakan metode Deep Learning dengan arsitektur Bidirectional Long Short-Term Memory (Bi-LSTM). Data ulasan dikumpulkan melalui teknik web scraping menggunakan perangkat lunak Tweet-Harvest dan dilabeli secara otomatis melalui pendekatan Ensemble Learning berbasis Transformer (IndoBERT, RoBERTa, dan BERT-SMSA) untuk mengatasi ketiadaan label pada data mentah. Model klasifikasi yang dikembangkan mengintegrasikan fitur Word Embedding FastText guna menangani karakteristik bahasa tidak baku dan kata-kata di luar kosakata (OOV) yang dominan di media sosial. Hasil pengujian menggunakan metode Stratified 5-Fold Cross Validation menunjukkan bahwa model yang diusulkan mampu mencapai akurasi rata-rata sebesar 96%, dengan nilai F1-Score pada kelas negatif mencapai 93%. Penelitian ini menyimpulkan bahwa kombinasi arsitektur Bi-LSTM dan teknik pelabelan otomatis terbukti efektif dalam merepresentasikan sentimen pengguna secara akurat dan konsisten sebagai acuan strategis bagi pengembangan layanan | |||||||||
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) |
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| Divisions: | 03-Fakultas Teknik > 55201-Jurusan Teknik Informatika | |||||||||
| Depositing User: | julkifli jul abdurahman | |||||||||
| Date Deposited: | 18 Feb 2026 07:48 | |||||||||
| Last Modified: | 18 Feb 2026 07:48 | |||||||||
| URI: | http://eprints.untirta.ac.id/id/eprint/58484 |
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