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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): June 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.
CONTINUOUS AUDITING AND EXPLAINABLE AI FOR ENHANCING REAL TIME FINANCIAL ANALYSIS Sheva Rani Wibowo; Agus Wibowo
Jurnal Akuntansi dan Bisnis Vol. 5 No. 2 (2025): Oktober 2025 : 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.v5i2.931

Abstract

The incorporation of machine learning (ML) into modern financial analysis has made transactions more complex and ondemand, and further increased the scope of ML applications in finance. On the other hand, accounting and auditing processes have yet to adopt machine learning systems due to challenges of precision, interpretability, and integration. This research analyzes the balance between accuracy and explainability in XAI for fraud detection with XGBoost, Transformer-Based Models, and continuous auditing approaches. Key findings suggest that although less preferred, Transformer-Based Models are more accurate in detecting multi-faceted fraud and deliver an AUC-ROC of 95%. XGBoost, with an AUC-ROC score of 92%, surpasses set benchmarks for continuous auditing, achieving high assurance while requiring low operational complexity, and therefore the model with fewer continuous auditing constraints. The results substantiate the premise that claiming compliance with audit requirements evokes low complexity emerges logic steeped in trust faced by agile controllers. The primary claim was the accompanied loss of understanding with defining accuracy and the adoption scrutiny processes of XGBoost. These results emphasize the potential of hybrid AI systems achieved by merging explainability of XGBoost with sequential analysis of Transformers which also tend to be less interpretable. Such models could benefit decision makers significantly.
ANALISA TREN KINERJA KEUANGAN DAN VALUASI SAHAM PT ACE HARDWARE INDONESIA TBK: STUDI LONGITUDINAL 2019-2023 Sabina Berlina Gulo; Agus Wibowo
Jurnal Akuntansi dan Bisnis Vol. 5 No. 2 (2025): Oktober 2025 : 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/erdqb520

Abstract

The increasingly competitive Indonesian capital market demands investors to be more meticulous in assessing company performance, not only through financial ratios but also trends over a specific period. This study aims to analyze the trends in financial performance and stock valuation of PT Ace Hardware Indonesia Tbk longitudinally during the 2019–2023 period, which includes the pre-, during, and post-COVID-19 pandemic phases. The research method used is descriptive and associative quantitative with secondary data from annual financial reports and stock prices. Data analysis includes descriptive statistics, classical assumption tests, and multiple linear regression. The results show fluctuations in financial performance, with profitability tending to recover post-pandemic, as well as volatility in stock valuation as reflected by PER and PBV. Although regression analysis indicated a consistent influence of the Debt to Equity Ratio (DER) on valuation, no variables were partially significant due to multicollinearity and sample limitations. The study concludes that the company demonstrated resilience in facing pandemic challenges, and highlights the importance of considering external factors and methodological limitations in interpreting results. The research implications contribute to investors and management in decision-making and provide a basis for further studies in the retail sector
Co-Authors Christina Effina Putri dwi Joseph Teguh Santoso Raspini Raspini Sabina Berlina Gulo Sheva Rani Wibowo Yan Qin