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p-Index From 2021 - 2026
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All Journal International Journal of Research and Applied Technology (INJURATECH)
Fahrezi, Muhamad
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A Systematic Literature Review: The Use of Artificial Intelligence and Machine Learning in Financial Risk Management and Predictive Analytics Fahrezi, Muhamad
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 1 (2024): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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This systematic literature review explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in financial risk management and predictive analytics by analyzing 20 peer-reviewed articles published between 2019 and 2025. From an initial pool of 131 articles, a rigorous screening process was conducted to ensure relevance and quality. The findings indicate that AI and ML have significantly enhanced the accuracy, speed, and adaptability of financial risk assessments, particularly in areas such as credit risk prediction, fraud detection, and market volatility forecasting. However, challenges such as lack of model transparency, limited implementation in real-world settings, and insufficient coverage of emerging markets remain prevalent. This review identifies future research opportunities including the development of explainable AI (XAI), alignment with regulatory frameworks, expansion into underexplored financial domains, and the creation of localized models for inclusive finance. Overall, AI and ML demonstrate transformative potential, but their effectiveness depends on responsible, context-aware, and interdisciplinary application
Blockchain Integration in Academic Credentialing: Implications for Employment Markets and Economic Growth Fahrezi, Muhamad
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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The integration of blockchain technology in academic credentialing has the potential to revolutionize traditional systems of verifying educational achievements. The study aims to assess how blockchain can improve the efficiency, transparency, and security of academic qualification verification processes. Using a qualitative research approach, we analyzed current trends and reviewed of blockchain applications in education. The results indicate that blockchain technology significantly reduces credential fraud, enhances labor market mobility, and accelerates hiring processes. Furthermore, blockchain’s decentralized nature allows for greater control and ownership of academic credentials, making them more accessible and verifiable across borders. These findings highlight the transformative potential of blockchain to create a more efficient, equitable, and globalized labor market. As a result, blockchain could not only streamline employment practices but also drive economic growth by improving human capital utilization and fostering global talent mobility
Data Driven Organizational Culture and Managerial Cognition Reshaping Decision Authority Capability Development and Governance Structures in Algorithmic Environments Fahrezi, Muhamad
International Journal of Research and Applied Technology (INJURATECH) Vol. 6 No. 1 (2026): June 2026 (Online First)
Publisher : Universitas Komputer Indonesia

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The rapid expansion of artificial intelligence, big data analytics, and algorithmic decision systems has transformed organizational structures and decision processes. This study analyzes how data-driven organizational culture and managerial cognition jointly reshape decision authority, capability development, and governance structures in algorithmic environments. Using a narrative literature review, the research integrates perspectives from strategic management, organizational theory, information systems, and digital governance to develop a comprehensive conceptual framework. The findings show that a data-driven culture promotes evidence-based norms that encourage managerial cognitive adaptation toward probabilistic reasoning and analytics literacy. This cognitive shift legitimizes decentralized yet algorithmically bounded decision authority characterized by shared human–machine accountability. Structural transformation further requires hybrid capabilities combining data literacy, cross-functional integration, and governance competence. Governance redesign institutionalizes oversight mechanisms that reinforce cultural alignment through continuous learning. The study advances understanding of the socio-cognitive foundations of algorithmic transformation and provides strategic guidance for building adaptive, accountable organizations in data-intensive environments.
Data Mining and Corporate Financial Distress Prediction: Integrating Classical Bankruptcy Models with Contemporary Machine Learning Approaches Fahrezi, Muhamad
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Corporate financial distress prediction has shifted from classical ratio based statistical models toward data driven machine learning systems, raising concerns regarding the trade-off between predictive accuracy and interpretability. This study evaluates the integration of classical bankruptcy models with contemporary machine learning approaches to develop a robust and transparent early warning framework. Using a Literature Review, peer reviewed studies indexed in Scopus, Web of Science, and IEEE Xplore were synthesized, focusing on comparisons between the Z score model developed by Edward Altman, logistic regression, and modern algorithms such as support vector machines, ensemble learning, and neural networks. The findings indicate that machine learning models, particularly ensemble methods, demonstrate superior predictive capability in capturing nonlinear financial relationships. However, traditional accounting indicators remain fundamental predictors of distress. The study concludes that a hybrid framework integrating accounting based theory with machine learning optimization offers the most effective and strategically sustainable approach to corporate risk assessment.
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