Journal of International Accounting, Taxation and Information Systems

The Role of Learning Experience and Religiosity on the Interest of Accounting Students to Pursue a Career in Islamic Financial Institutions

Yoni Saskia — 2025-05-05

This research is motivated by the rapid development of the Islamic finance industry in Indonesia over recent decades, which has created attractive career opportunities for accounting graduates. As accounting students generally possess strong competencies in finance, and several universities have incorporated Islamic accounting into their curricula, this sector presents a relevant professional pathway. This study investigates the influence of learning experience and religiosity on accounting students career interest in Islamic financial institutions, with information overload and alternative attractiveness as moderating variables. Using Structural Equation Modeling (SEM), data were collected from 100 university students in Indonesia. The study's findings, rooted in the Theory of Planned Behavior and Cognitive Load Theory, demonstrate that both learning experiences and religiosity significantly influence career interests. Interestingly, an overload of information tends to weaken the relationship between religiosity and career interest, but it does not affect the bond between learning experiences and career interest. Additionally, the allure of alternative options can reduce the connection between religiosity and career interest, while leaving the link between learning experiences and career interest intact.

Artificial Intelligence for Cybersecurity: A Comprehensive Analysis of Algorithms, Frameworks, and Real-World Applications

Saidamin Sajid — 2025-05-06

The rapid rise in cyber threats has necessitated the integration of Artificial Intelligence (AI) to enhance cybersecurity strategies. This study aims to examine the effectiveness of AI algorithms in detecting and mitigating cyber threats, analyze AI-driven frameworks for cybersecurity operations, and assess real-world applications and challenges in deployment. A qualitative methodology was employed through a systematic literature review of 30 peer-reviewed articles published between 2021 and 2025, sourced from academic databases such as IEEE Xplore, ScienceDirect, Springer, and Wiley Online Library. Data extraction and screening were guided by the PRISMA protocol to ensure the inclusion of high-quality, relevant studies. Results indicate that AI techniques such as neural networks, support vector machines, and deep learning are highly effective in identifying anomalies, detecting intrusions, and analyzing malware. Furthermore, AI-based cybersecurity architectures are increasingly adaptive, scalable, and integrated with real-time response systems. However, challenges remain in model explainability, data privacy, and adversarial attacks.The study concludes that while AI significantly improves cybersecurity capabilities, its deployment must be guided by ethical, legal, and operational considerations. Future research should focus on improving model transparency and developing adaptive defense mechanisms.

Application of Classification Algorithm on Financial Data to Improve Financial Distress Prediction

Dea Amellia Kartika Dewi — 2025-05-06

The COVID-19 pandemic has intensified financial distress across various industries in Indonesia, especially in sectors like Accommodation and food & beverage, Other services, and Transportation & Warehousing. This situation highlights the urgent need for an accurate early warning system that can predict financial distress using reliable classification algorithms for business continuity. This research compares the Performance of the Support Vector Machine (SVM) and Decision Tree classification algorithms in predicting financial distress. The study utilizes secondary data from annual financial reports of companies listed on the Indonesia Stock Exchange (IDX) from 2019 to 2023. The research focuses on the Accommodation and food & beverage, Other services, and Transportation & Warehousing sectors. Data is collected using a purposive sampling method, ensuring balance across observations. A quantitative data analysis approach with an experimental design is applied to evaluate the classification performance. The results indicate that the Decision Tree algorithm outperforms SVM in all key Performance metrics: accuracy, precision, recall, and F1-score. The Decision Tree achieves perfect classification results, while SVM exhibits lower predictive ability, particularly in recall and F1-score. These findings suggest that the Decision Tree is more effective for financial distress prediction in this dataset. The study contributes to financial risk assessment by demonstrating the practical application of machine learning in financial classification tasks. Future research can enhance this model by incorporating larger datasets and developing application-based implementations to improve decision-making processes in corporate financial management.

The Influence of Audit Experience, Audit Digitalization, Audit Fees and Quality of Audit Results as Intervening Variables on Survey Audit Performance at Public Accounting Firms in Central Java

Djoko Kristianto — 2025-05-21

This study investigates the effects of audit experience, digitalization, audit fees, and audit quality on auditor performance, focusing on public accounting firms in Central Java. Using data from 49 respondents, the research emphasizes the role of financial reports as a communication tool between companies and stakeholders. Audit fees are influenced by assignment risks, service complexity, and the required expertise. Discrepancies in fee determination may indicate reduced auditor motivation. Auditor experience varies, impacting performance and professional conduct. More experienced auditors tend to demonstrate higher professionalism, which often correlates with higher fees and better audit quality. The findings reveal that audit experience, digitalization, and audit fees significantly affect auditor performance. Additionally, high-quality audit results also contribute positively to performance outcomes. Effective audits demand adequate resources, strong adherence to standards, and mastery of digital tools to ensure thorough and efficient processes. Ultimately, auditors who possess both technical expertise and ethical understanding are better equipped to deliver reliable audit services.

The Effect of Internal Control, Risk Management, and Whistleblowing Systems on Fraud Prevention

Dea Marseli — 2025-05-29

This study aims to determine the effects of internal control, risk management, and whistleblowing on fraud prevention. The research sample included as many as 75 respondents, including structural officials within OPD. Quantitative research using the survey approach method was used. The secondary data used were questionnaires distributed to the Lebong district OPD officials. The data analysis method used was multiple linear regression using SPSS version 29. This study results the internal control variable significantly affects fraud prevention, the risk management variable has no significant effect on fraud prevention, and the whistleblowing system variable significantly affects fraud prevention. This study provides practical insights for government organizations to strengthen fraud prevention strategies through better monitoring and reporting mechanisms. In addition, risk management needs to be reviewed to be more effective in reducing potential fraud. By implementing the right strategy, OPD can create a more transparent and accountable financial and operational management environment

Key Performance Indicators and Employee Performance: A Systematic Literature Review

Cindy Pramita — 2025-06-05

This research focuses on Key Performance Indicators (KPI) and employee performance in companies. This study uses a Systematic Literature Review scheme aimed at reviewing previous research related to Key Performance Indicators and performance. There were 1,100 research literatures found and then filtered using the PRISMA method. Using the PICO method in data collection from 2 sources, namely Google Scholar and Crossref. The filtering and data inclusion results obtained 30 journals as material for the final review. Through this research, it is concluded that the implementation of performance appraisal can use tools such as Key Performance Indicators to make it easier for company leaders to assess employee performance. Key Performance Indicators can help companies improve efficiency in assessing employee performance. The recommendation from this research is that companies must improve technology to support Key Performance Indicators for assessing employee performance so that the assessment is more accurate.

The Strategic Role of Machine learning Algorithms in Bolstering Cybersecurity and Resilience

Khudai Qul Khaliqyar — 2025-06-09

The rapid evolution of cyber threats in recent years has intensified the need for intelligent and adaptive security measures. Machine learning (ML) has emerged as a promising solution, offering capabilities for real-time threat detection, prediction, and autonomous response. This systematic literature review aims to investigate the effectiveness of various machine learning algorithms in enhancing cybersecurity between 2018 and 2025. Using a predefined search strategy, articles were sourced from reputable databases including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. The review focused on peer-reviewed research examining the application of ML in cybersecurity contexts such as threat detection, cyber resilience, and automated incident response. A total of 25 studies were selected after applying strict inclusion and exclusion criteria. The analysis revealed that deep learning and ensemble methods showed superior performance in detecting complex threats, while supervised learning was prevalent in intrusion detection systems. However, issues like data imbalance, adversarial attacks, and ethical transparency were noted as significant challenges. The findings underscore the transformative role of ML in cybersecurity, yet emphasize the need for interpretability and ethical oversight. This review concludes that integrating ML with existing defense systems and human expertise is essential for building adaptive, resilient, and ethical cybersecurity solutions in the evolving digital landscape.

Accounting Students' Perceptions of a Career as a Public Accountant: Job Market Considerations, Work Environment, Workload, and Financial Rewards

Amanda Putri Dwi Anjani — 2025-06-10

The Indonesian Institute of Public Accountants (IAPI) reports that there is a significant lack of personnel in the field of public accounting. This is reflected in the accounting profession's key role in instilling professional principles, integrity, and competence that are not only intelligent in calculations but also must understand the economic situation at large and can present an assessment in the form of information that can be used for decision-making. This scenario highlights the importance of students in addressing the growing demand for public accountants in Indonesia. The primary objective of this research is to identify discrepancies in past studies on how job market factors, work environment, workload, and financial incentives shape accounting students' perception of a career as public accountants. The methodology employed in this study involves a quantitative strategy, and the target population consists of PTN registered in the LLDIKTI region of Region II. Based on the results of this study, it is known that the implications of variables of consideration of the job market, work environment, workload, and financial rewards support the theory of planned behavior, and it is important for students to consider and know how their perception of a profession is, such as the possibility of getting a job in that career, the circumstances and conditions of the workplace environment they will encounter, the pressure of the work done, and the reward or salary to be received. Future research can enhance this model by adding moderation variables and expanding the sample for more accurate results.

Artificial Intelligence in Cybersecurity: A Comparative Study of Threat Detection Algorithms

Shir Ahmad Hamidi — 2025-06-11

This paper presents a systematic literature review (SLR) on AI-based algorithms for cybersecurity threat detection, aiming to evaluate the effectiveness and performance differences of various artificial intelligence techniques. The purpose of this study is to provide a comprehensive overview of the most effective AI models for detecting cyber threats and to examine their practical applications across various cybersecurity domains, including IoT, critical infrastructure, and cyber-physical systems. The review includes studies published between 2021 and 2025, sourced from prominent academic databases such as MDPI, SpringerLink, and IEEE Xplore.The methodology employed involved the selection of peer-reviewed articles using inclusion and exclusion criteria, followed by thematic analysis of the AI techniques used in the studies. Key themes such as supervised learning, unsupervised learning, deep learning, and hybrid approaches were explored. Performance metrics including accuracy, precision, recall, F1-score, and false positive rates were used to evaluate algorithm effectiveness. The results highlight the comparative performance of different AI models and provide insights into the strengths and weaknesses of each approach, as well as their suitability for specific cybersecurity applications.The findings emphasize the importance of dataset quality, algorithm transparency, and the need for reducing false positives in real-world applications. The review concludes by recommending the continued development of hybrid AI approaches and the need for more transparent, explainable models.

Analysis of Sales Budget Preparation in Preparing Production Cost Budgets (A Study of Home Industry UD Usaha Jaya)

Dion Yanuarmawan — 2025-06-22

This study aims to analyze the preparation of the sales budget used in preparing the production cost budget at UD Usaha Jaya. The data used consist of company data from the previous three years. This study is a type of quantitative research using both primary and secondary data. The Least Square method is used for sales forecasting and separating semi-variable costs. The results indicate that the 2024 sales budget served as the basis for the production cost budget, which includes raw material costs, direct labor, and factory overhead. However, the study found that the preparation of both sales and production cost budgets did not follow standardized procedures, leading to significant discrepancies between planned and actual figures. A key weakness of this study is its limited scope, focusing on a single home industry, which may reduce the generalizability of the findings. Additionally, reliance on historical internal data may not fully capture market dynamics or external factors affecting demand. The study recommends that the company adopt a more structured approach to budgeting and consider integrating digital budgeting tools to improve forecasting accuracy and cost control.

The Effect of Current Ratio (CR) and Debt To Equity Ratio (DER) on Return on Assets (RoA) in Pharmaceutical Subsector Companies Listed on the Indonesian Stock Exchange (IDX) 2020-2023 Period

Wahyu Indah Mursalini — 2025-07-29

The primary objective of this research is to gather practical evidence concerning the impact of the Current Ratio and Debt To Equity Ratio on the Return On Assets in Pharmaceutical Subsector Companies listed on the Indonesia Stock Exchange (IDX) between 2020 and 2023. The research employs regression analysis using a quantitative methodology based on financial reports of the companies. The research sample was chosen through the purposive sampling technique, consisting of 26 pharmaceutical subsector companies that fulfilled the research criteria. The findings of the study suggest that the CR has a positive and influential influence on ROA, with a regression coefficient of 0.023 and a significance level of 0.000 (<0.05), hence confirming the hypothesis (H₁). Conversely, the DER is uninfluential on ROA, with a regression coefficient of 0.011 and a significance value of 0.596 (>0.05), leading to the rejection of hypothesis H₂. Nevertheless, both the CR and DER jointly is influential on the ROA, with an F value of 15.785 and a significance of 0.000, supporting the acceptance of hypothesis H₃. Hence, this study highlights the significant role of the CR in enhancing ROA, while the DER lacks individual significance.

Financial Access and Financial Literacy on the Performance of MSMEs in Solok City

Seflidiana Roza — 2025-07-30

This research aims to examine the influence of financial inclusion and financial literacy on the performance of Micro, Small, and Medium Enterprises (MSMEs), specifically 3 kg LPG gas depots located in Bukit Sundi and Kubung Districts, Solok Regency. MSMEs in this area still face various obstacles, particularly limited access to formal financial services and low understanding of business operators regarding financial management. The study employs a quantitative method utilizing multiple linear regression to analyze primary data obtained from surveys completed by 40 gas depot owners. Findings indicate that financial literacy positively impacts MSME performance, although financial inclusion does not have a significant partial effect. Nevertheless, when considered together, both variables significantly influence business performance. This finding confirms the importance of improving financial literacy for MSME operators rather than merely expanding access to financial services. This research recommends that future studies consider additional variables such as work incentives, human resource quality, and productivity to obtain a more comprehensive understanding of the factors that influence MSME performance.