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FAKTOR YANG MEMPENGARUHI KINERJA KEUANGAN PERUSAHAAN PADA PERUSAHAAN PROPERTY DAN REAL ESTATE Vidyarto Nugroho; Nicholas Nicholas
Jurnal Bina Akuntansi Vol 7 No 1 (2020): Jurnal Bina Akuntansi Vol.7 No.1 2020
Publisher : Sekolah Tinggi Ilmu Ekonomi Wiyatamandala

The purpose of this research is to analyze the effect of leverage, liquidity, firm size,and firm age oncompanies financial performance in property and real estate companies listed on Indonesia Stock Exchange in 2015 – 2017. The sampling method used purposive sampling with total sample 105 data which was financial statement from www.idx.co.id. The collected data were analyzed by using Eviews 10. The research result that independent variables describe the dependent variable up to 72.54% , while 27.46% is described by other factors. Independent variable leverage, firm size, and firm age do not have a significant effect on firm value, while the liquidity have a significant effect on companies financial performance.

Analysis of the Impact of Corporate Governance Mechanisms on Earnings Management: A Study of Pharmaceutical Companies from 2019 to 2024 Firman Syah; Vidyarto Nugroho
Devotion : Journal of Research and Community Service Vol. 7 No. 7 (2026): Devotion: Journal of Community Research
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/devotion.v7i7.25728

This research aimed to analyze the influence of corporate governance mechanisms on earnings management in pharmaceutical companies listed on the Indonesia Stock Exchange (IDX) during the 2019–2024 period. The corporate governance mechanisms examined in this study were proxied by audit committee size, audit committee expertise, and board of directors’ size. Using a quantitative approach with secondary data obtained from annual reports and financial statements, the sample consisted of 9 pharmaceutical companies selected through purposive sampling, resulting in 54 panel observations. Data analysis was conducted using panel data regression with cluster-robust standard error estimation to improve the reliability of the test results. Earnings management was measured using the Modified Jones Model through discretionary accruals. The results showed that audit committee size, audit committee expertise, and board of directors’ size did not have a significant effect on earnings management. These findings indicate that the structural corporate governance mechanisms examined in this study were not effective in limiting or explaining earnings management practices in pharmaceutical companies during the observation period. This suggests that the effectiveness of corporate governance is determined not only by structural characteristics but also by other factors, such as supervisory independence, audit quality, company-specific characteristics, and business environment conditions. Future research is encouraged to expand the sample size, include additional corporate governance variables, and apply alternative earnings management measurement models to obtain more comprehensive findings.

Machine Learning-Based Detection of Financial Statement Fraud: Integrating Beneish Ratios, Linguistics, and Stock Volatility on the Indonesia Stock Exchange Abdurrochman Halomoan Hasibuan; Vidyarto Nugroho
Devotion : Journal of Research and Community Service Vol. 7 No. 7 (2026): Devotion: Journal of Community Research
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/devotion.v7i7.25731

This research aimed to develop and evaluate a financial statement fraud (FSF) detection model for companies listed on the Indonesia Stock Exchange (IDX) during the 2024–2025 period using a machine learning approach. An ablation study design was employed to test 27 model combinations across three feature scenarios: Beneish M-Score financial ratios, linguistic features extracted from Management Discussion and Analysis (MD&A) texts using the InSet lexicon, and 30-day stock price volatility. Nine classification algorithms were evaluated using precision, recall, and F1-score metrics. The best-performing model combined financial ratios and linguistic features using Gradient Boosting, achieving an F1-score of 0.545. In contrast, the addition of stock price volatility as a feature did not improve model performance and instead reduced the classification ability of all tested algorithms. These findings indicate that the Indonesian capital market may have limited ability to anticipate indications of financial statement fraud before related information is publicly disclosed. This study concludes that integrating accounting and linguistic information is more effective than relying solely on financial ratios or incorporating market data. These findings contribute to the development of machine learning-based FSF detection literature in Indonesia and provide an alternative approach for auditors, investors, and regulators to enhance the effectiveness of early financial statement fraud detection.

Analysis of the Influence of the Amount of Aid Funds, Regulations, and the Size of Political Parties on the Compliance of Financial Information Disclosure via the Internet (Internet Financial Reporting) Ghina Sausan Fadiyah; Vidyarto Nugroho
Devotion : Journal of Research and Community Service Vol. 7 No. 7 (2026): Devotion: Journal of Community Research
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/devotion.v7i7.25732

This research aimed to empirically examine the influence of the amount of assistance funds, regulations, and political party size on compliance with Internet-based financial information disclosure (Internet Financial Reporting [IFR]) among national political parties in Indonesia, as well as to test the moderating role of political party size. This study employed a quantitative causal-associative design using Binary Logistic Regression on 45 panel observations from nine national political parties during the 2020–2024 period, selected through purposive sampling. Data were obtained from BPK audit reports, KPU decisions, and official party websites, and analyzed using EViews 9. The results showed that the amount of assistance funds had a positive and significant effect on IFR disclosure compliance, with a coefficient of +30.5824 and a p-value of 0.0369, while regulations did not have a significant effect, with a coefficient of +0.1600 and a p-value of 0.8157. Political party size had a positive and significant effect on IFR disclosure compliance, with a coefficient of +146.3055 and a p-value of 0.0436. Furthermore, political party size moderated the relationship between assistance funds and IFR disclosure compliance, with a negative moderating effect indicated by a coefficient of −6.3926 and a p-value of 0.0406. The model produced a McFadden R-squared value of 0.1793 and a prediction accuracy rate of 64.44%. These findings indicate that IFR compliance is influenced by stewardship responsibilities and public visibility pressures, whereas formal regulations alone are insufficient to encourage substantive behavioral changes. The negative moderating effect suggests the presence of diminishing returns and potential bureaucratic inertia within larger political parties.

Comparative Analysis of the Altman Z-Score and Beneish M-Score Methods in Detecting Financial Statement Fraud: A Case Study on Mining Sector Companies Listed on the IDX for the 2018-2024 Period Gita Olivia Sitompul; Vidyarto Nugroho
Devotion : Journal of Research and Community Service Vol. 7 No. 7 (2026): Devotion: Journal of Community Research
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/devotion.v7i7.25736

Financial statement fraud remains a significant threat to capital market integrity, with mining companies being particularly vulnerable due to commodity price volatility and complex accounting practices. This study compared the effectiveness of the Altman Z-Score and Beneish M-Score methods in detecting financial statement fraud (FSF) among mining companies listed on the Indonesia Stock Exchange (IDX) during the 2018–2024 period. Using an associative quantitative approach with 168 panel data observations, the results showed that the Altman Z-Score had a negative and significant effect on FSF, whereas the Beneish M-Score did not have a significant effect. The superior performance of the Z-Score was confirmed through evaluation metrics, including accuracy of 77.98%, precision of 94.34%, specificity of 96.43%, and an AUC value of 0.9025, compared with the Beneish M-Score, which achieved only 47.02% accuracy and an AUC value of 0.4157, indicating performance below random classification. These findings indicate that the Altman Z-Score, through its financial distress dimension, is more relevant and reliable as an early detection instrument for financial statement fraud in the Indonesian mining sector.

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