Klasifikasi Ulasan Aplikasi E-KTP Menggunakan Bidirectional Encoder Respresentations from Transformers - Rusdi, Alfida Hari; Tentua, Meilany Nonsi (Vortex, 2026)
The Digital Population Identity application (E-KTP Digital) is part of e-government development aimed at improving the quality of public services. However, user reviews on the Google Play Store are still grouped based on star ratings, so the level of user satisfaction is not yet described in depth. This study aims to classify the sentiment of user reviews of the E-KTP Digital application using the Bidirectional Encoder Representations from Transformers (BERT) method with the Multilingual BERT (mBERT) model. A total of 15,000 reviews were collected from July 3, 2023 to May 31, 2025 and filtered into 1,750 reviews through data cleaning and manual labeling processes. The dataset is divided into training and testing data with ratios of 60:40, 70:30, and 80:20. The training process is conducted using the AdamW optimizer for 4 epochs with a batch size of 16. Model evaluation is planned using accuracy, precision, recall, and F1-score metrics to measure the performance of user review sentiment classification.