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*, Madani Hatta — Mon, 05 May 2025 00:00:00 +0000

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*, Eid Mohammad Ibrahimi, Baryali Raoufi — Tue, 06 May 2025 00:00:00 +0000

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*, Sriwidharmanely — Tue, 06 May 2025 00:00:00 +0000

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.