Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal Journal of Digital Learning and Distance Education Journal of Social Studies and Education Journal for Science and Religious Studies
Moses Adeolu AGOI
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Religion Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Education Immunology & microbiology Languange, Linguistic, Communication & Media Social Sciences Other

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Navigating Educational Challenges in Indonesia: Policy Recommendations for Future Success Sain, Zohaib Hassan; Aulia Luqman AZIZ; Moses Adeolu AGOI
JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION Vol. 3 No. 4 (2024): Journal of Digital Learning and Distance Education (JDLDE)
Publisher : RADINKA JAYA UTAMA PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56778/jdlde.v3i4.339

Abstract

Domestic and international observers scrutinize education in Indonesia due to persistent challenges that significantly diminish its quality. This study aims to provide a constructive analysis by identifying key issues within the Indonesian educational sector and proposing various strategic solutions to enhance the system. Utilizing a qualitative methodology, this research incorporates a literature review along with the collection of both primary and secondary data. The data collection involves extensive documentation and subsequent analysis. The findings reveal multiple pressing issues in Indonesian education, such as disparities in educational outcomes, substandard educational facilities and infrastructure, deficiencies in teacher quality and conduct, issues with student morality, and a lack of tolerance for ethnic diversity. Addressing these problems requires a collaborative effort across all societal sectors. Proposed solutions include enhancing teacher and student welfare, cultivating a global perspective and fostering a positive mindset among educators and learners, designing an inclusive and holistic curriculum, ensuring vigilant management of budgets, and encouraging greater involvement from the private sector. By incorporating these strategies, there is a potential not only to rectify current deficiencies but also to elevate the overall standard of education in Indonesia. This improvement will require sustained efforts and a commitment to innovation and inclusivity in educational practices.
Integrating Technology in the Fight Against Human Trafficking: Challenges, Opportunities, and Social Implications Moses Adeolu AGOI; Sain, Zohaib Hassan; Oluwanifemi Opeyemi AGOI
Journal of Social Studies and Education Vol. 2 No. 2 (2024): July-December (2024)
Publisher : Al-Qalam Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61987/jsse.v2i2.452

Abstract

This research focuses on the role of technology in detecting and supporting the recovery of victims of human trafficking. In a global context, human trafficking is a complex and hidden crime, which is often difficult for authorities to tackle. Technology, such as artificial intelligence (AI) and facial recognition, is increasingly relied upon to identify victims and improve response speed in the recovery process. This study uses a qualitative research design with a case study approach to explore how technology is applied by law enforcement agencies and non-governmental organizations (NGOs). The main data sources were obtained through in-depth interviews, observations, and documentation from various sources who were directly involved in efforts to utilize technology in combating human trafficking. The results show that technology, despite its challenges in terms of implementation and sustainability, has proven to be effective in raising public awareness, speeding up the detection process, and supporting victim recovery through the provision of safe and accessible services. The implications of this study show that the integration of technology can strengthen prevention and rehabilitation efforts in dealing with human trafficking, while empowering the community to play a more active role in reporting such incidents. With ever-evolving technological advancements, opportunities to eradicate human trafficking are increasingly open, providing hope for previously neglected victims.
Clinical Decision Support Systems for Dementia Management Using Predictive Analytics and Explainable AI Moses Adeolu Agoi; Gbenga Oyewole Ogunsanwo; Bamidele Olumuyiwa Alaba; Maulana, Asep Surya
Averroes: Journal for Science and Religious Studies Vol. 3 No. 01 (2026)
Publisher : Yayasan Insan Cendekia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62446/averroes.030105

Abstract

Research Background: Dementia is a growing global public health challenge driven by population ageing and increased life expectancy. Clinical Decision Support Systems (CDSS) have emerged as important tools to assist clinicians in early diagnosis, risk stratification, prognosis estimation, and personalized care planning in dementia management. Recent advances in predictive analytics and artificial intelligence (AI), particularly machine learning and deep learning models, have significantly enhanced the analytical capabilities of CDSS. However, the integration of these technologies into clinical practice remains limited due to concerns related to interpretability, generalizability, and ethical accountability. This study aims to review the development of CDSS for dementia management that integrate predictive analytics with Explainable Artificial Intelligence (XAI). Methods: A systematic literature review was conducted using peer-reviewed publications from major academic databases published between 2017 and 2025. The analysis focuses on algorithmic approaches, data sources, validation strategies, and explainability techniques applied in contemporary dementia CDSS. Key Findings: The findings indicate that predictive models demonstrate high accuracy in detecting early cognitive impairment and predicting disease progression. Nevertheless, their clinical implementation is often constrained by the “black-box” nature of many AI models and limited external validation. Explainable AI methods such as SHAP, LIME, and attention-based networks are increasingly used to improve transparency and clinician trust. Contribution: This study contributes an integrative perspective that emphasizes the importance of balancing predictive performance with interpretability, ethical governance, and clinical usability. Conclusion: It concludes that integrating predictive analytics with XAI is essential for developing trustworthy and clinically applicable CDSS in dementia care.
Co-Authors Aulia Luqman Aziz Bamidele Olumuyiwa Alaba Gbenga Oyewole Ogunsanwo Maulana, Asep Surya Oluwanifemi Opeyemi AGOI Sain, Zohaib Hassan