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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL AKUNTANSI DAN BISNIS
Agus Wibowo
Universitas Sains Dan Teknologi Komputer
Computer Science & IT Economics, Econometrics & Finance Engineering

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DIGITAL TECHNOLOGY AND PRIVACY REGULATIONS IN FINANCIAL ACCOUNTING: AN EMPIRICAL STUDY USING INSTITUTIONAL THEORY AND TAM Raspini Raspini; Agus Wibowo
Jurnal Akuntansi dan Bisnis Vol. 6 No. 1 (2026): Mei 2026 : Jurnal Akuntansi Dan Bisnis(AKUNTANSI)
Publisher : LPPM PoliteknikPratamaKendal- Universitas Sains Dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jiab.v6i1.977

Abstract

The rapid advancement of digital technology, coupled with the tightening of data privacy regulations, has significantly impacted financial accounting practices. However, empirical studies examining how these regulations influence accounting processes remain limited. The novelty of this research lies in its integrative approach, combining Institutional Theory and the Technology Acceptance Model (TAM) to analyze the role of digital technology and privacy regulations in transforming financial accounting. By employing a mixed-methods research design, this study integrates quantitative analysis of financial reports and surveys with qualitative interviews and case studies. The quantutative component measures the impact of privacy regulations on financial reporting and compliance, while the qualitative analysis explores the challenges faced by accountants and auditors in implementing these regulations, as well as the adaptive strategies they employ. The integration of Institutional Theory helpes explain external pressures, such as regulatory and professional demands, while TAM provides insight into the internal acceptance of digital tools by accounting professionals. This study contributes to bridging the empirical gap in accounting research by offering a comprehensive understanding of organizational responses to technological innovation and regulatory environment in the digital era.
INTEGRATING AI –DRIVEN ECONOMIC FORECASTING IN CORPORATE FINANCIAL DECISIONS: A DECISION THEORY PERSPECTIVE Christina Effina Putri dwi; Agus Wibowo
Jurnal Akuntansi dan Bisnis Vol. 6 No. 1 (2026): Mei 2026 : Jurnal Akuntansi Dan Bisnis(AKUNTANSI)
Publisher : LPPM PoliteknikPratamaKendal- Universitas Sains Dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jiab.v6i1.989

Abstract

Global economic uncertainty has pushed companies to make financial decisions that are faster, more accurate, and adaptable. However, the use of Artificial Intelligence (AI) to predict economic conditions in real-time and support corporate financial decisions is still limited-especially in developing countries. This study aims to design an AI-based model capable of forecasting global economic fluctuations and analyzing its impact on corporate financial strategy. A qualitative approach was applied, using case studies and descriptive-exploratory analysis based on secondary data and relevant AI system documentation. The results show that the AI model can identify key economic indicators and provide predictions that enhance financial planning accuracy. Moreover, the model strengthens decision-making by combining data-driven strategies with managerial intuition. This research contributes both theoretically and practically to the use of AI in supporting financial decision-making, particularly in volatile economic environments.
Predicting Mandarin Vocabulary Learning Outcomes Using Data-Driven Machine Learning Yan Qin; Joseph Teguh Santoso; Agus Wibowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7143

Abstract

The rapid expansion of language learning in higher education highlights the need for data-driven approaches to monitor student progress and provide timely instructional support. This study aims to develop a predictive framework for Mandarin vocabulary mastery using supervised machine learning. A dataset of 147 undergraduate students was analyzed, incorporating study hours, number of exercises, pre-test scores, and attendance as predictors of learning outcomes. Logistic Regression, Random Forest, and XGBoost algorithms were trained and evaluated, with XGBoost achieving the highest performance (accuracy 88%, F1-score 0.88), demonstrating its superior ability to capture complex learning patterns. Analysis of feature importance revealed that pre-test scores and the number of exercises were the most influential predictors of student success. Furthermore, a prototype graphical user interface (GUI) was developed to visualize predictions in real time, enabling instructors to identify at-risk students and adjust teaching strategies accordingly. The novelty of this study lies in integrating predictive analytics with pedagogical applications, bridging machine learning and educational practice. Beyond its technical contributions, this research provides practical insights for higher education stakeholders, showing how predictive models can support early intervention, enhance curriculum design, and promote evidence-based decision-making in Mandarin vocabulary instruction.
Co-Authors Christina Effina Putri dwi Joseph Teguh Santoso Raspini Raspini Yan Qin