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All Journal Indonesian Journal of Information System IJID (International Journal on Informatics for Development) Journal of Information Systems and Informatics Indonesian Journal of Innovation and Applied Sciences (IJIAS) The Indonesian Journal of Computer Science
Maguraushe, Kudakwashe
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences

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Journal : The Indonesian Journal of Computer Science

 1 
Machine Learning and Explainable AI for Parkinson’s Disease Prediction: A Systematic Review Ndlovu, Belinda; Maguraushe, Kudakwashe; Mabikwa, Otis
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4837

Abstract

Parkinson's disease is a movement disorder within the nervous system that impacts millions of people across the world. The standard diagnostic methods usually miss early subtle signs of disease which has driven research into Machine Learning (ML) and Explainable Artificial Intelligence (XAI) to develop better predictive models. Following PRISMA guidelines we analyzed 13 studies found in IEEE Xplore, PubMed and ACM concerning different ML methodologies for Parkinson’s disease prediction. Deep learning models using vocal and motor data achieve good accuracy but require more clinical trust and transparency due to their opaque "black-box" nature. SHAP and LIME act as XAI solutions that address transparency issues in model predictions by delivering understandable information regarding model outputs to all users. Current solutions show progress. However, there are multiple complications, including limited and unbalanced datasets alongside accuracy-explainability trade-offs which underline the need for extensive datasets, multidisciplinary teamwork and practical validation.
Framework for Enhancing Interoperability, Data Exchange, and Security in Healthcare through Blockchain Technology Muderere, Vimbai Alice; Ndlovu, Belinda; Maguraushe, Kudakwashe
The Indonesian Journal of Computer Science Vol. 14 No. 4 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i4.4950

Abstract

The healthcare sector is changing, such as fragmentation issues, the sharing of data, and the security of protected health information. Traditional systems tend to work independently or in silos, resulting in disjointed patient records and system inefficiency. With more trusted healthcare providers, patients relying more on digital solutions than ever, the urgency for a consistent data management solution has never been greater. This systematic literature review (SLR) aims to investigate the existing framework, factors, opportunities and challenges of blockchain technology in healthcare systems. The integrative approach was done according to the PRISMA guidelines. A literature search was carried out on various electronic databases, including PubMed, IEE Xplore, and ACM Digital Library, which gave a total of 832 articles, to begin with. Based on set scale criteria, 18 studies were deemed relevant for analysis. The findings indicate that blockchain technology holds promise due to its ability to facilitate secure and easy data sharing through immutability, cryptographic methods, and the removal of centralized authorities. However, there is a challenge of interoperability, data exchange and security within the healthcare systems and other technologies. This study contributes to the body of knowledge by developing a conceptual framework that helps policymakers, researchers, and practitioners that act as guide to effectively implement blockchain technology in healthcare. The framework addresses key considerations of traditional systems, such as scalability, interoperability, security, and regulatory compliance, and offers a structured approach to resolving current challenges.
Ethical Adoption of AI-Powered EdTech in Higher Education: Human-AI Interaction through an Ethically Extended UTAUT2 Model Masimba, Fine; Maguraushe, Kudakwashe; Chimbo, Bester
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i1.5079

Abstract

This study addresses the need for responsible AI adoption in higher education by developing a human-centred ethical extension of the UTAUT2 model. It integrates two new constructs; AI fairness and human autonomy support and three ethical moderators: ethical risk awareness, perceived algorithm bias and user autonomy concern. To validate the framework, an empirical investigation was conducted with 400 respondents using a structured questionnaire, with data analyzed via regression. All sixteen hypotheses were supported. The model demonstrated strong predictive power, explaining 72.2% of the variance in behavioural intention and 69.1% in use behaviour. The results provide meaningful insights into how ethical perceptions influence adoption. Ultimately, the framework offers practical guidance for policymakers, educators and developers to ensure fair, trustworthy and human-centric AI integration in learning environments.
Co-Authors Chamboko, Hazel Chimbo, Bester Dube, Sibusisiwe Hadebe, Sanele Mabikwa, Otis Masimba, Fine Mnguni, Nompilo Makhosi Muderere, Vimbai Alice Mutanga, Murimo Bethel Ndlovu, Belinda Ndlovu, Sharmaine Ngwazi, Tinashe Nkomo, Nkululeko