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All Journal Bulletin of Computer Science Research Jurnal Informatika dan Rekayasa Perangkat Lunak
Bagastian, Bagastian
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Analisis Sentimen Publik terhadap ‘Save Raja Ampat’ di Media Sosial Menggunakan Model IndoBERT Eko Putro, Dimas; Juarsa, Doris; Putra Hermana, BP; Bagastian, Bagastian; Sulistiani, Heni
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.621

Abstract

The "Save Raja Ampat" campaign has emerged as a significant environmental issue that has garnered widespread public attention on social media platforms, particularly TikTok and YouTube. Videos tagged with #SaveRajaAmpat have sparked various public responses, ranging from full support to criticism of natural resource exploitation. This phenomenon highlights the importance of understanding public sentiment as an indicator of the campaign's effectiveness. This study aims to analyze public sentiment toward the campaign using a language modeling approach based on artificial intelligence, namely IndoBERT. The data were obtained from user comments on TikTok videos promoting the “Save Raja Ampat” campaign, totaling 10,000 comments. The analysis process involved several stages, including data preprocessing, sentiment labeling (positive, negative, neutral), and the training and evaluation of the IndoBERT model. Preliminary results indicate that the majority of public sentiment toward the campaign is positive, with the model achieving an accuracy rate of 71% in sentiment classification. This study contributes to understanding public perception of environmental issues and demonstrates the effectiveness of using the IndoBERT model in the context of social media.
Transforming the Data Ecosystem through Machine Learning and Artificial Intelligence: A Systematic Review of Innovative Big Data Frameworks Bagastian, Bagastian; Putro, Dimas Eko; Fudholi, Muhammad Fahmi; Suryono, Ryan Randy
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Volume 7 Number 1 March 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i1.1437

Abstract

The digital revolution era has created fundamental transformation in data management and utilization, where machine learning and artificial intelligence integration becomes the primary catalyst in optimizing contemporary data ecosystems. Global data volume predicted to reach 181 zettabytes by 2025 demands innovative approaches in big data management, yet 80% of organizations still experience difficulties integrating AI technology with their existing data infrastructure. This research aims to identify and analyze characteristics of innovative frameworks that integrate machine learning and artificial intelligence in data ecosystem transformation, and formulate comprehensive framework recommendations for the future. The research method employs a qualitative approach with Systematic Literature Review (SLR) on 2021-2022 publications via Google Scholar, with thematic analysis using Critical Appraisal Skills Program (CASP) checklist. Research results identify eight major innovative frameworks including AI for Smart Society 5.0, Big Data-AI-IoT Integration, to Digital Responsibility Accounting, with main characteristics of process automation capabilities, service personalization, edge computing for real-time decision making, and blockchain implementation for data security. Implementation challenges include digital infrastructure limitations, human resource skill gaps, data security, and organizational resistance. Transformation impact proves significant in education, governance, and business intelligence sectors. The conclusion shows that comprehensive future frameworks must be adaptive, ethical, and sustainable by integrating technology, human, and environmental dimensions in a balanced manner. A phased implementation approach is recommended with priority on strengthening digital infrastructure and developing human resource competencies through cross-sector collaboration.
Co-Authors Eko Putro, Dimas Fudholi, Muhammad Fahmi Heni Sulistiani Juarsa, Doris Putra Hermana, BP Putro, Dimas Eko Ryan Randy Suryono