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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Syntax Literate: Jurnal Ilmiah Indonesia JURIKOM (Jurnal Riset Komputer) JETL (Journal Of Education, Teaching and Learning) JP-BSI (Jurnal Pendidikan Bahasa dan Sastra Indonesia) Disastra: Jurnal Pendidikan Bahasa dan Sastra Indonesia Jurnal Abdi Insani Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Tarling : Journal of Language Education JAPI: Jurnal Akses Pengabdian Indonesia Educalitra: English Education, Linguistics, and Literature Journal eProceedings of Engineering INOVTEK Polbeng - Seri Informatika Disastra: Jurnal Pendidikan Bahasa dan Sastra Indonesia Solusi dan Inovasi Pengabdian Kepada Masyarakat Governance IT Adoption and Technology Advance
Hari Widi Utomo
Universitas Telkom
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Sentiment Analysis of Pertamax on Social Media and MyPertaminta Data Using the IndoBert Algorithm Dzulfan Yumna Azis; Anthony Dewantoro; Kumara Galan Pramana; Mahazam Afrad; Hari Widi Utomo
Governance IT Adoption and Technology Advance Vol. 1 No. 2 (2026)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/govita.v1i2.11679

Abstract

The rapid growth of digital services in Indonesia has accelerated the adoption of online platforms for fuel distribution through the MyPertamina application developed by PT Pertamina. Public responses toward the application and related fuel distribution policies are widely expressed through social media and application reviews. This study aims to analyze public sentiment toward MyPertamina using multi-platform data collected from Google Play Store, Instagram, and Twitter. The research employed a Natural Language Processing approach using the Transformer-based IndoBERT model. The methodology included data collection, data integration, text preprocessing, sentiment labeling, model fine-tuning, performance evaluation, and result visualization. The collected textual data were classified into positive and negative sentiment categories to represent public opinion. Experimental results showed that IndoBERT achieved an accuracy of 92.04%, with balanced precision, recall, and F1-score values. These findings demonstrate that IndoBERT effectively handles unstructured and informal Indonesian text from multiple digital platforms. Overall, integrating multi-platform data with IndoBERT-based sentiment analysis provides comprehensive insights into public perceptions of MyPertamina and supports strategic decisions. Future studies should expand data sources, increase dataset size, compare additional Transformer models, and evaluate broader sentiment patterns across diverse digital environments effectively.
Co-Authors Abednego Dwi Septiadi Achmad Sultoni Achmad Sultoni Achmad Sultoni Aghnia NurJumala Agung Wicaksono Alfarizy, Shehal Sigi Alika, Shintia Dwi Anthony Dewantoro Arba, Raihan Adi Arif Riyandi Aslam Hadil Matin Brawijaya, Brawijaya Darmansah Dhina Setyowati Diah Ayu Lestari Dyah Ayu Indri Hapsari Dyah Monika Ismawanti Dzulfan Yumna Azis Febriani, Atik Gilang Hukma Rahmanda GITA FADILA FITRIANA Huda, Farhat Khorn Chanpiriza Kumara Galan Pramana Leksono, M. Lukman Libertiana Saragih, Nopri Anita M. Lukman Leksono Mahazam Afrad Maria Naibaho Nahar, Muhamad Amrizal Nicolaus Euclides Wahyu Nugroho Nirwana, Zahra Fikri Ayu Novian Adi Prasetyo Prastiwi, Nanda Sesty Rafika Nur, Yohani Setiya Resad Setyadi Riyatno Riyatno Safiri, Satria Rizki Satria Rizki Safiri Shintia Dwi Alika Sidanti, Risang Windu Suryatiningsih Syifa , Fikra Titan Tatsuya Kusakabe Titis Widiyani Wahyu Andi Saputra Wulan Cahya, Putri Zuleffa, Mazia