Article Per Year (5 Year)
p-Index From 2021 - 2026
1.67
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This Author published in this journals
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
Ndlovu, Belinda
Unknown Affiliation
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 : Journal of Information Systems and Informatics

 1 
A Comparative Analysis of Machine Learning Techniques and Explainable AI on Voice Biomarkers for Effective Parkinson’s Disease Prediction Ndlovu, Belinda; Maguraushe, Kudakwashe; Mabikwa, Otis
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1172

Abstract

Parkinson's disease (PD) is a neurological movement disorder that remains difficult to diagnose, although it affects millions globally. Early diagnosis can lead to more effective and improved patient outcomes. Diagnosis through traditional methods is subjective and often lacks transparency, raising concerns about reliability. In this study, the CRISP-DM framework was applied to compare eight ML algorithms, including Random Forest and Support Vector Machine (SVM). Recursive Feature Elimination (RFE) was used to preprocess, balance, refine the data and find the eight most predictive vocal features. With 195 recordings coming from the UCI Parkinson’s Speech Dataset, which contains voice measurements from 31 individuals (23 with PD and 8 healthy controls), Random Forest (Entropy) had the best performance (F₁ = 96.6%, ROC AUC = 0.98). Explainable AI tools (SHAP and LIME) were integrated, allowing both global and instance-level understanding of model predictions thereby identifying measures of pitch variability (MDVP: RAP, spread1, PPE) as key predictors of PD. This research contributes to the practical deployment of reliable, transparent PD prediction tools in real-world medical settings, supporting early diagnosis and improved patient care. This raises the issue of the urgent need to detect PD early among Africa's aging populations to help protect the cultural heritage contained in the voices of the elders. this research contributes to the practical deployment of reliable, transparent PD prediction tools in real-world medical settings, supporting early diagnosis and improved patient care.Future work should embark on validating these findings over much more varied cohorts, integrating additional data modalities (e.g., gait, imaging), and enhancing model robustness. Real-time speech analysis-based tools, in the end, will allow remote screening, early intervention, and tailored care.
Blockchain Adoption in Healthcare: Enhancing Interoperability, Security and Data Exchange Muderere, Vimbai Alice; Ndlovu, Belinda; Maguraushe, Kudakwashe
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1267

Abstract

Fragmented data across the healthcare industry increasingly impedes interoperability, compromises data security, and ultimately interferes with safe and quality patient care delivery. This research introduces a framework that uses blockchain technology to enhance interoperability and data exchange in healthcare environments. Leveraging qualitative methods,semi-structured interviews were held with fifteen health care practitioners at various facilities who gave their insights and perceptions of data sharing and blockchain technology. The findings were thematic and conceptualized through the Technology Acceptance Model, focusing on perceived ease of use and perceived usefulness, and the Technology-Organization-Environment framework that examined organizational support and regulatory compliance. Thematic analysis identified four main themes, including (i) factors influencing adoption: ease of use with four participants, usefulness with three participants, organizational support with two participants, regulatory compliance with two participants, and technical infrastructure with two participants. (ii)Application areas included patient data management, billing and payment, and remote patient monitoring; (iii) benefits such as a more effective decentralized system, safer storage of data, and patient empowerment. (iv)Challenges included privacy concerns, the costs of implementation and system failure, and patients' knowledge and stakeholders' digital literacy. The findings suggested that stakeholders knew the potential disruption to any blockchain system. However, major issues needed to be addressed before implementation. This research expands the conversation about innovative solutions to health care interoperability. It exposes potential ways to address the challenges to adoption. Recommendations for future research include examining the scalability and integration of blockchain technology across different healthcare environments and addressing the pressing need for empirical evidence regarding its real-world applications and impacts.
Quantum Computing Cryptography: A Systematic Review of Innovations, Applications, Challenges, and Algorithms Maitireni, Peter; Ncube, Vusumuzi; Ndlovu, Belinda; Sibanda, Thando
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1331

Abstract

This study explores how to build quantum-resistant systems to safeguard digital infrastructure in the post-quantum era by uncovering the innovations, applications, algorithms, and challenges of Quantum Computing cryptography. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach a search was conducted across the following databases for the years 2021–2025: PubMed, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. We shortlisted 15 studies from 519 screened articles for a comprehensive evaluation based on their relevance. Findings show strong adoption in finance, healthcare, IoT, cybersecurity, and e-government, with lattice-based PQC emerging as the most dominant cryptographic family, followed by QKD and hybrid PQC–QKD models. The review highlights key obstacles, including transition complexity, lack of global standards, high implementation costs, and integration difficulty. The study contributes by providing the first sector-aligned synthesis of innovations, identifying algorithmic trends, and mapping global research disparities through a conceptual model. It also presents a structured set of future research directions to guide policymakers, cryptographers, and practitioners preparing for quantum-enabled threats.
Co-Authors Chamboko, Hazel Chipfumbu, Colletor T. Dube, Sibusisiwe Dube, Yangekile Hadebe, Sanele Kiwa, Fungai Jacqueline Mabikwa, Otis Maguraushe, Kudakwashe Maitireni, Peter Marambi, Sheltar Muderere, Vimbai Alice Muduva, Martin Muzenda, Hillary Ncube, Vusumuzi Ndlovu, Sharmaine Ngwazi, Tinashe Nyamupaguma, Tanyaradzwa Sibanda, Thando Vareta, Passmore