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User Satisfaction Analysis of the Website Using the E-Servqual Method Mazia Zuleffa; Hari Widi Utomo; Arif Riyandi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/m9a6r812

Abstract

This study aims to specifically analyze the key service quality dimensions—Efficiency, Privacy, and Contact—that influence user satisfaction with the PMB website of Madyathika Polytechnic. A structured questionnaire based on the E-SERVQUAL model was distributed to respondents, and the collected data were analyzed using descriptive statistics, SPSS-based validity and reliability testing, and Importance Performance Analysis (IPA). The findings reveal that although several service dimensions meet user expectations, attributes such as cross-device accessibility, user data privacy, and clarity of contact information still show negative service quality gaps. These results provide a foundation for targeted recommendations to improve the overall digital service experience. This research contributes to the strategic enhancement of digital service quality in higher education admissions systems.
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.