Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal ORGANIZE: Journal of Economics, Management and Finance Journal of Applied Accounting
Fitriyeni Oktavia
Universitas Andalas
Economics, Econometrics & Finance

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Capital Structure Decisions In Capital-Intensive Firms: Evidence From Indonesian Listed Firms Nindya Farah Dwi Puspitasari; Eka Wirajuang Daurrohmah; Fitriyeni Oktavia; Novta Winkey Pradana
ORGANIZE: Journal of Economics, Management and Finance Vol. 5 No. 3 (2026): Economic Transformation and Development
Publisher : Perkumpulan Dosen Fakultas Agama Islam Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58355/organize.v5i3.225

Abstract

Asset-heavy and capital-intensive firms require substantial financing for long-term investment, making capital structure decisions particularly important. This study explores the link between operating leverage, profitability, liquidity, and inflation on capital structure. Using balanced panel data from 65 firms across five capital-intensive sectors listed on the Indonesia Stock Exchange, the study analyzes 390 firm-year observations over six years. The Random Effect Model was selected based on the Chow, Hausman, and Breusch-Pagan Lagrange Multiplier tests. The analysis indicates that profitability and liquidity are both inversely associated with capital structure, and these associations are statistically significant, whereas degree of operating leverage and inflation have no significant effects. These findings indicate that profitability and liquidity have a stronger influence in explaining leverage and should receive greater consideration in capital structure policies.
Data Driven Approach for Detecting Financial Statement Fraud: (A Systematic Literature Review) Fitriyeni Oktavia; Rossy Endah Permatasari
Journal of Applied Accounting Vol. 4 No. 2 (2025): Journal of Applied Accounting
Publisher : Indonesia Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158//jaa.v4i2.1472

Abstract

Financial statement fraud represents a persistent and complex threat to organizational integrity, requiring more advanced analytical tools to detect subtle accounting manipulations. To provide an evidence-based understanding of how data-driven techniques have been utilized in this domain, this study conducts a Systematic Literature Review (SLR) guided by the PRISMA framework. The review addresses two research questions: (a) what data-driven approaches have been used to detect financial statement fraud, and (b) what are the characteristics of the data, modelling methods, and evaluation metrics employed in prior studies. A structured search and screening process was executed using predefined inclusion and exclusion criteria, enabling the selection of relevant peer-reviewed studies from multiple academic databases. The included articles were further examined using meta-analysis techniques to synthesize quantitative evidence where applicable. The findings reveal that financial statement fraud detection has increasingly shifted toward machine learning, deep learning, graph-based analytics, and other advanced data-driven models capable of identifying hidden or non-linear patterns in financial reporting data. The reviewed studies employ diverse data characteristics, including financial ratios, earnings indicators, transactional records, and graph-structured relationships. Overall, this review highlights both the advancements and the methodological challenges within the field, underscoring the need for improved data quality, consistent evaluation practices, and models that balance predictive performance with interpretability for auditing applications.  Keywords: Fraud, data-driven, machine learning, anomaly detection, deep learning.
Co-Authors Eka Wirajuang Daurrohmah Nindya Farah Dwi Puspitasari Novta Winkey Pradana Rossy Endah Permatasari