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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknik Komputer AMIK BSI Jusikom : Jurnal Sistem Komputer Musirawas JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA KOMPUTA : Jurnal Ilmiah Komputer dan Informatika JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Digital Teknologi Informasi Jurnal Pengabdian kepada Masyarakat Nusantara Prosiding Seminar Riset Mahasiswa Jurnal Teknologi Informasi Mura Jurnal Edukasi EJESH : Journal Islamic Economics and Social Jurnal UNIV.BI MENGABDI
sobri, ahmad
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Implementation of BERT in Sentiment Analysis of National Digital Samsat (SIGNAL) User Reviews Based on Machine Learning Savitri, Ratna; Rizki, Fido; Sobri, Ahmad
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.32059

Abstract

The SIGNAL application facilitates online vehicle tax payments for the public. The application's quality is frequently evaluated through user reviews on platforms like the Google Play Store. This study aims to analyze the sentiment of SIGNAL user reviews using a Machine Learning-based approach, specifically the BERT (Bidirectional Encoder Representations from Transformers) model. The dataset consists of 20,000 user reviews. After preprocessing, the remaining data comprises 17,287 reviews, categorized into 12,758 positive reviews, 2,160 neutral reviews, and 2,369 negative reviews. To address data imbalance, the Random Over Sampling (ROS) technique was applied. The evaluation was performed using metrics such as accuracy, precision, recall, and F1-score. The results of the study indicate that the IndoBERT model can classify sentiments with an accuracy of 99% and a validation accuracy of 98% after five epochs of training. Confusion matrix analysis shows that the model achieved an overall accuracy of 99.72% on training data and 98.68% on testing data. This study demonstrates that the IndoBERT model is highly effective in classifying sentiment and makes a significant contribution to understanding the user experience of SIGNAL, which can serve as a foundation for future improvements to the application.
Co-Authors Aktavera, Beni Andri Anto Tri Susilo, Andri Anto Aprillian, Alif Armanto, Armanto Athallah, Muhammad Rafif aviani, tri hasanah bimastari Bayani, Ahmad Bimastari, Tri Hasanah Daulay, Nelly Khairaini Dwi Budi Santoso Elmayati, Elmayati Fadila, Nurul Fitri, Maysade Hidayat, Asep Toyib Intan, Bunga Irawan, Davit Karman, Joni Lestari, Novi Lingga Wijaya, Harma Oktafia Lukman Hakim Maya Sari, Wisdalia Muhammad Husein Nurdiana, Hestin Nurdiansyah, Deni Padilah, Salwa Rahmawati, Diah Ayu Regina Regina Rizki, Fido Rizqa, Rachma Fatihatul Robby, Robby Rudi Kurniawan Rusdiyanto Rusdiyanto, Rusdiyanto Sari, Wisdalia Maya Savitri, Ratna Sunardi, Lukman Surya Putra, Raihan Rama Syahvini, Salsabila Nasywa Wulandari, Cindi