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All Journal JURISMA: Jurnal Riset Bisnis & Manajemen INVOICE : JURNAL ILMU AKUNTANSI JURNAL SATYA MANDIRI MANAJEMEN DAN BISNIS
Agustine Dwianika
Universitas Terbuka
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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FROM COMPLIANCE TO CREDIBILITY: A BIBLIOMETRIC REVIEW OF REGULATORY FRAMEWORKS AND EXTERNAL ASSURANCE IN SUSTAINABILITY REPORTING Edi Purwanto; Agustine Dwianika
Jurnal Satya Mandiri Manajemen dan Bisnis Vol 11 No 1 (2025): Volume 11 Nomor 1 Tahun 2025
Publisher : Pasca Sarjana Universitas Satya Negara Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54964/satyamandiri.v11i1.492

Abstract

This study conducts a comprehensive bibliometric analysis to map global research trends on regulatory frameworks, external assurance statements, and corporate sustainability reporting. Drawing on 2,468 documents from the Scopus database, it employs VOSviewer to visualize publication trends, co-authorship networks, citation patterns, and keyword co-occurrence. Results indicate a growing scholarly focus, especially from 2015 onwards, coinciding with regulatory developments and heightened sustainability expectations. The United States, United Kingdom, and European countries dominate contributions, while the Global South remains underrepresented. Influential authors such as Milne and Gray and Tsalis are identified as foundational voices. Thematic evolution reveals a shift from normative discussions toward technical and policy-driven inquiries. The study highlights emerging topics such as digital assurance and ESG integration while calling for greater inclusivity and interdisciplinary engagement. It provides critical insights for scholars, regulators, and practitioners committed to advancing credible, impactful sustainability reporting.
Enhancing Student Feedback Analysis: NLP-Based Machine Learning Classification of Satisfaction Levels at Universitas Terbuka Surakarta Agustine Dwianika; Ratih Paramitasari
JURISMA : Jurnal Riset Bisnis & Manajemen Vol. 15 No. 2: Oktober 2025
Publisher : Program Studi Manajemen, Fakultas Ekonomi dan Bisnis, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jurisma.v15i2.18093

Abstract

This research aims to classify the level of student satisfaction at Universitas Terbuka (UT) Surakarta using Natural Language Processing (NLP) techniques and machine learning algorithms. The study utilizes textual responses from student satisfaction surveys and processes them through a supervised classification approach. By applying methods such as TF-IDF for feature extraction and classification algorithms like Naïve Bayes, Support Vector Machine (SVM), and Random Forest, the research seeks to identify which algorithm best categorizes sentiment into satisfaction levels. Results indicate that the SVM model outperforms other algorithms in accuracy, precision, and F1-score. This approach demonstrates the practical application of NLP in higher education quality assurance and offers valuable insights for policymakers. Keywords: Machine Learning; Natural Language Processing; Student Satisfaction; Classification; Sentiment Analysis
Unlocking Sustainability Performance through Artificial Intelligence and Green Intellectual Capital Agustine Dwianika; Hendrian; Diana Frederica; Temy Setiawan; Hendro Lukman
Invoice : Jurnal Ilmu Akuntansi Vol. 8 No. 2 (2026): September 2026
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/165r7071

Abstract

The increasing pressure to achieve environmental sustainability has accelerated organizational adoption of Artificial Intelligence (AI) to support digital transformation, environmental governance, and sustainability-oriented innovation. However, previous studies have largely examined Artificial Intelligence and Green Intellectual Capital (GIC) independently, with most focusing on conceptual discussions, sector-specific contexts, or isolated sustainability outcomes, leaving limited empirical evidence on how these constructs interact to enhance sustainability performance, particularly in developing countries. Addressing this gap, this study proposes and empirically tests an integrated framework that examines the relationships among Artificial Intelligence, Green Intellectual Capital, and sustainability performance. A quantitative approach was employed using primary data collected through questionnaires from 120 academics, sustainability practitioners, and corporate managers involved in digital transformation and environmental governance. Purposive sampling was applied, and the data were analyzed using multiple linear regression with SPSS version 26. The results indicate that Artificial Intelligence significantly strengthens Green Intellectual Capital, while Green Intellectual Capital positively influences sustainability performance. Furthermore, the integration of Artificial Intelligence and Green Intellectual Capital significantly enhances sustainability performance, with the proposed model explaining 78.1% of the variance in sustainability performance (R² = 0.781). These findings demonstrate that Artificial Intelligence functions as a strategic enabler that strengthens environmental knowledge, green innovation, and sustainability governance through the development of Green Intellectual Capital. The study contributes to the sustainability accounting and digital transformation literature by providing integrated empirical evidence that extends current understanding of how technological capability and sustainability-oriented intellectual resources jointly drive long-term organizational sustainability and competitive advantage, particularly within the context of developing economies.
Co-Authors Diana Frederica Hendrian Hendro Lukman Purwanto, Edi Ratih Paramitasari, Ratih Temy Setiawan, Temy