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All Journal TEKNIK INFORMATIKA J@TI (TEKNIK INDUSTRI) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah Teknik Industri JURNAL PENDIDIKAN TAMBUSAI Jurnal Review Pendidikan dan Pengajaran (JRPP) Dinasti International Journal of Education Management and Social Science JATI (Jurnal Mahasiswa Teknik Informatika) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Manajemen Pendidikan dan Ilmu Sosial (JMPIS) Jurnal Abdimas Kartika Wijayakusuma Innovative: Journal Of Social Science Research JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Land Journal Competitive Spektrum Industri
Erna Mulyati
Program Studi Teknik Industri Politeknik Pos Indonesia, Jl. Sariasih No.54, Bandung
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Public Health Social Sciences Transportation Other

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Journal : TEKNIK INFORMATIKA

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Sentiment Analysis of Pospay Application Reviews Using the Bert Deep Learning Method Mulyati, Erna; Muhammad Ibnu Choldun Rachmatullah; Adri Sapta Firmansyah
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.41116

Abstract

E-money usage in Indonesia has grown significantly due to increasing internet penetration and smartphone adoption. Digital transactions are becoming more common, with platforms like GoPay, OVO, and Dana leading the market. The government and financial institutions actively support this shift through regulations and initiatives. This study analyzes user sentiment on the Pospay application using the BERT deep learning method, based on 16,760 Google Play Store reviews. To the best of our knowledge, this is the first study to apply BERT for sentiment analysis of Pospay user reviews in Indonesia. The goal is to understand user perceptions and satisfaction. BERT helps capture subtle nuances in reviews, including informal expressions and abbreviations like "gk" for negative sentiment. The model achieves high accuracy, with precision scores of 0.82 (negative) and 0.93 (positive), and recall scores of 0.92 (negative) and 0.93 (positive). Findings suggest PT Pos should enhance application stability, security, transaction processing, and customer service. Regular updates are recommended to improve performance and user satisfaction.
Co-Authors Adawiyah, Anida Adri Sapta Firmansyah Aghitsna Iqbal Alif, Aghitsna Iqbal Agus Purnomo Agus Purnomo Alfian, Muhammad Andrey Casmadi, Yohanes Dani Ramdani, Dani Dodi Permadi, Dodi Firdhaus, Saputra Aulia Hamidin, Dini Hidayat, Syamsu Kencana Verawati Krisnawanti Kusdanu, Saptono Lasmana, Hendri Lusel, Rikardo Riki Made Prima Widyapurna Mandasari, Cindy Maniah, Maniah Muhammad Amin Muhammad Ibnu Choldun Rachmatullah Mustafia*, Mustafia Nizar, Catur Ahaddi Noviana Noviana Pardede, Nia Reswadi, Oshelby Riyadi, Faris Akbar Saputra, Hijrah Simbolon, Justin Goklas Sondakh, Eduard Sugesti, Hesti Wahyudi, Arif Yudha Waskito, Saptono Kusdanu