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All Journal JURNAL PENDIDIKAN JASMANI DAN OLAHRAGA Justek : Jurnal Sains Dan Teknologi JURTEKSI KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat COLLASE (Creative of Learning Students Elementary Education) Jurnal Teknologi Dan Sistem Informasi Bisnis ILKOMNIKA: Journal of Computer Science and Applied Informatics International Journal of Advances in Data and Information Systems Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Abmas Negeri (JAGRI) TIERS Information Technology Journal Jurnal Minfo Polgan (JMP) Indonesian Journal of Digital Business Joong-Ki : Jurnal Pengabdian Masyarakat Journal of Scientific Research, Education, and Technology Jurnal Ilmiah Pengabdian dan Inovasi Madani: Multidisciplinary Scientific Journal Jurnal E-bussiness Institut Teknologi dan Bisnis Muhammadiyah Polewali Mandar Joong-Ki Jurnal Transformasi Bisnis Digital The Indonesian Journal of Computer Science Impression : Jurnal Teknologi dan Informasi J-CEKI Ulil Albab Joong-Ki INOVTEK Polbeng - Seri Informatika Journal of Information Technology
Rangga Gelar Guntara
Universitas Pendidikan Indonesia
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Journal : INOVTEK Polbeng - Seri Informatika

 1 
Sentiment Analysis of LinkAja Digital Wallet Application Reviews on Google Play Store using Transfer Learning IndoBERT Sandy Sanjaya; Rangga Gelar Guntara; Syti Sarah Maesaroh
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/afjx7b16

Abstract

The LinkAja digital wallet receives an average rating of 3.5 on the Google Play Store despite having a higher number of user reviews than its competitors, indicating a strong need for data-driven evaluation of user satisfaction. This study performs sentiment classification on LinkAja user reviews using the IndoBERT model implemented within the CRISP-DM framework. A total of 1,483 reviews posted from January 1 to May 31, 2025, were analyzed through automatic labeling using a pretrained IndoBERT sentiment model and validated using an 80:20 hold-out scheme. Model performance was evaluated using accuracy, the F1 score, and the Matthews Correlation Coefficient (MCC) to address class imbalance. The results show high classification performance with 95% accuracy, a macro F1-score of 0.92, a weighted F1-score of 0.94, and an MCC of 0.90. Sentiment distribution reveals a dominance of negative sentiments at 59.5%, followed by positive (26.1%) and neutral (14.4%) sentiments. Theoretically, this study reinforces the superiority of IndoBERT over conventional machine learning methods for Indonesian sentiment analysis. Practically, the findings provide actionable insights into service improvements, particularly regarding transaction stability and system reliability.
Comparative Analysis of Machine Learning Models for BUMN Bank Stock Sentiment Classification During Danantara Formation Period Hafizha Nurul Qolby; Rangga Gelar Guntara; Syti Sarah Maesaroh
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/91z79392

Abstract

Discussions about state-owned bank stocks (BBRI, BBNI, and BMRI) on platform X intensified during the formation of Danantara. However, the correlation between social media sentiment and stock movements remains weak due to high noise levels and potential buzzer activity. This study combines sentiment and text similarity analyses (cosine similarity) to identify repeated communication patterns in discussions related to state-owned bank stocks. A total of 1,086 tweets were manually labeled and verified by two independent validators Text features were represented using TF–IDF and evaluated through four classical machine learning algorithms: Naïve Bayes, Logistic Regression, Support Vector Machine, and XGBoost. The model was validated using a hold-out scheme (80:20) and assessed with a confusion matrix. The sentiment distribution of the dataset shows 53% negative and 47% positive tweets Logistic Regression achieved the highest accuracy of 66%. The cosine similarity analysis identified 1.8% of tweets with similarity ≥0.90, indicating limited recurring communication patterns. These findings suggest that integrating sentiment and text similarity analyses can serve as an initial approach to detect indications of coordinated activity and to understand public opinion dynamics toward state-owned bank stocks.
Co-Authors Adam Hermawan Adam Hermawan Adi Prehanto Andrian Wijaya Anugerah Tirta Kenanga Unggul Mitra Mandiri Asep Nuryadin Az Zahra Maulaa Habiibah Az Zahra Maulaa Habiibah Benyamin Hartanto Btari Mariska Purwaamijaya Daud Fernando Dede Akhmad Nawawi Elis Solihati Fariha Nilan Fazry Rachman Susanto Ferdiansyah Frans Budi Kashira Hafizha Nurul Qolby Herdiana, Oding Krisna Sujaya Laras Bellaning Restu M. Yakub Maesaroh, Syti Sarah Mesa Rahmi Stephani Moch Gan-gan Sidiq Muhammad Dzikri Ar Ridlo Muhammad Farhan Hanif Effendi Muhammad Naufal Nurfirmansyah Muhammad Rahmanda Muhammad Rijal Wahid Muharram Muhammad Rizki Nugraha Muhammad Rizky Ginanjar Nandang Rusmana Novian Rachman Iskandar Nur Azizyah Putri Dewita Nur, Lutfi Oding Herdiana Riska Aprilia Rizky Ditya A Rachman Rosarina Giyartini Rudi Salam Sandy Sanjaya Soni Santana Stella Kova Suci Ramasiah Suryana, Dodi Teuku Khairul Amri Varinia Azkarin Varinia Azkarin Velisa Stevannie yogi prasetyo