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Riska Aryanti
Universitas Bina Sarana Informatika, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : Profitabilitas

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Analisis Sentimen Pengguna GoPay pada Layanan Keuangan Digital dengan Perbandingan Naïve Bayes dan SVM Dian Ardiansyah; Riska Aryanti; Eka Fitriani; Royadi
PROFITABILITAS Vol 5 No 2 (2025): JURNAL PROFITABILITAS
Publisher : Sistem Informasi Akuntansi Kampu Kabupaten Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/profitabilitas.v5i2.11513

Abstract

The rapid development of digital financial services has led to increased use of digital wallets, one of which is the GoPay application, resulting in a large volume of user reviews. These reviews contain valuable information regarding user satisfaction and service-related issues, making automated methods necessary to accurately analyze user sentiment. This study aims to analyze sentiment in GoPay user reviews and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms for sentiment classification.This research uses a dataset of 132,393 GoPay user reviews obtained from the Kaggle platform. The data are labeled based on user ratings into three sentiment classes: positive, neutral, and negative. The research stages include text preprocessing, feature transformation using the Term Frequency–Inverse Document Frequency (TF-IDF) method, sentiment classification using the Naïve Bayes and SVM algorithms, and model performance evaluation using accuracy, precision, recall, and F1-score metrics.The results show that 79.2% of the reviews are classified as positive, 17.1% as negative, and 3.7% as neutral. Based on performance evaluation, the SVM algorithm demonstrates superior results with an accuracy of 90.65%, precision of 90.7%, recall of 90.65%, and F1-score of 89.05%, compared to Naïve Bayes, which achieves an accuracy of 87.89%, precision of 89.1%, recall of 87.89%, and F1-score of 88.42%. These findings indicate that SVM is a more optimal method for sentiment analysis of GoPay user reviews, while Naïve Bayes remains an efficient and competitive alternative for large-scale text classification.
Co-Authors Agus Junaidi Agustiani, Sarifah Aldian Mauluda Alif Rizqi Mulyawan Andi Saryoko Andreas Roy Prasetya Ari Sulistiyawati Ari Sulistiyawati Arifin, Yosep Tajul Arman Ramadhani Asriyani Sagiyanto ASRIYANI SAGIYANTO, ASRIYANI Atang Saepudin Atang Saepudin Atang Saepudin Azis, Munawar Abdul Bayu Kusuma Ilyasa Universitas Bina Sarana Informatika Dahlia Dahlia Darma Setiawan Putra Dede Firmansyah Dede Firmansyah Saefudin Dedi Darwis Deni Gunawan Diah Puspitasari Dian Ardiansyah Dian Ardiansyah Dyah Ayu Megawaty Eka Dyah Setyaningsih Eka Fitriani Eka Fitriani Eka Fitriani Eka Fitriyani Fachri, Muhamad Faruk Ulum Haliza Ramadhanti, Pristya Harefa, Kristine Haryani Hasan, Fuad Nur Henny Leidiyana Herdian Pratama I Gede Iwan Sudipa Irfan Ridwan Jananto Watori Junhai Wang Kamil, Anton Abdul Basah KOMALASARI, YULI Martenia, Rina Masngud Megawaty, Dyah Ayu Mesran, Mesran Mochamad Wahyudi Oktaviyani Oktaviyani Oprasto, Raditya Rimbawan Pasaribu, A. Ferico Octaviansyah Perani Rosyani Pristya Haliza Ramadhanti Rachilsyah Ramdhani Efendi Rahmat Hidayat Rahmat Hidayat Ramadhani, Arya Richardus Eko Indrajit Rifky Permana Rifqi Rizaldi Rina Martenia Rizqi Nur Esmeralda Rosiun Universitas Bina Sarana Informatika Roy Prasetya, Andreas Royadi Royadi - Royadi Royadi Royadi, Royadi Salman Alfarizi SALMAN ALFARIZI Samudi Sari Dewi Universitas Bina Sarana Informatika PSDKU Pontianak Setiawansyah Setiawansyah Siti Khotimatul Wildah Siti Marlina, Siti Sopiyan Dalis Sumanto Sumanto Titik Misriati tri wahyuni Tri Wahyuni Ulum, Faruk Wahyudi, Agung Deni Walim Walim Wang, Junhai Yanto, Andika Bayu Hasta Yarimani Laia