Garuda - Garba Rujukan Digital

Artificial Intelligence Chatbots in Education: Academics Beliefs, Concerns and Pathways for Integration Ndlovu, Belinda; Ndlovu, Sharmaine; Dube, Sibusisiwe; Maguraushe, Kudakwashe
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10805

Although globally there are mixed perceptions regarding the academic integrity of chatbots, existing research has mainly focused on developed nations, neglecting the unique perspectives of academics in developing countries, with different contextual, environmental, and technological settings. This study presents lecturers’ perceptions of using Artificial Intelligence (AI) chatbots in education. Guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this research collected quantitative and qualitative data from 140 lecturers and three administrators from a STEM-based Zimbabwean university. The research confirmed that performance expectancy (belief in improved efficiency and personalised learning) and perceived value and social influence drive adoption. Contrary to previous studies, there was no significant link between effort expectancy (reduced workload) and chatbot use. Demographics like gender, age, and qualifications did not impact chatbot use. Academics were cautiously optimistic, recognising benefits like personalised learning and routine task management but concerned about ease of use, technical expertise, and ethical considerations. To effectively integrate AI chatbots into higher education processes, there is a need for funding, technical support, training, strengthening IT infrastructure and establishing frameworks for responsible use. Emphasising efficiency, personalisation, and robust support will help overcome barriers and maximise AI chatbots’ potential in education.

Managing Diabetes Using Machine Learning and Digital Twins Hadebe, Sanele; Ndlovu, Belinda; Maguraushe, Kudakwashe
Indonesian Journal of Innovation and Applied Sciences (IJIAS) Vol. 5 No. 2 (2025): June-September
Publisher : CV. Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47540/ijias.v5i2.1981

Diabetes is a major public health problem worldwide, and early diagnosis will remain pivotal for intervention and management. This Systematic Literature Review (SLR), therefore, attempts to explore the prospects of integrating Machine Learning (ML) and Digital Twins (DT) to enable diabetes treatment through prediction and patient-specific modeling. This SLR contributes to the body of literature by examining how ML and DTs are being applied in diabetes treatment, identifying the opportunities and challenges that exist, and determining which algorithms are most commonly used. In contrast to SLRs that have been reviewed previously, this study considers Digital Twin-based technological perspectives, along with algorithmic evaluations of ML models, to provide an overall view of the potential for combined use in diabetes care. Following PRISMA guidelines, 11 relevant studies were selected from major academic databases. The study identified Random Forests, Gradient-Boosted Decision Trees, K-Nearest Neighbors, Time Series and Structured Analysis, Regression-based algorithms, and Artificial Neural Networks as machine learning algorithms commonly used to predict diabetes risk. The integration of ML and DT for diabetes management enables the personalization of patient management through virtual representations, real-time monitoring of an individual's glucose levels, simulation of disease progression, and prediction of subsequent treatment steps for proactive and immediate decision-making. Through this collaboration, simulations of various situations are performed, and the interventions are optimized to correspond with unique human physiological profiles for better patient outcomes. Based on the results, policymakers must balance data quality and patient privacy.

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

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

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.

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

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.

Artificial Intelligence Chatbots in Education: Academics Beliefs, Concerns and Pathways for Integration Ndlovu, Belinda; Ndlovu, Sharmaine; Dube, Sibusisiwe; Maguraushe, Kudakwashe
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10805

Although globally there are mixed perceptions regarding the academic integrity of chatbots, existing research has mainly focused on developed nations, neglecting the unique perspectives of academics in developing countries, with different contextual, environmental, and technological settings. This study presents lecturers’ perceptions of using Artificial Intelligence (AI) chatbots in education. Guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this research collected quantitative and qualitative data from 140 lecturers and three administrators from a STEM-based Zimbabwean university. The research confirmed that performance expectancy (belief in improved efficiency and personalised learning) and perceived value and social influence drive adoption. Contrary to previous studies, there was no significant link between effort expectancy (reduced workload) and chatbot use. Demographics like gender, age, and qualifications did not impact chatbot use. Academics were cautiously optimistic, recognising benefits like personalised learning and routine task management but concerned about ease of use, technical expertise, and ethical considerations. To effectively integrate AI chatbots into higher education processes, there is a need for funding, technical support, training, strengthening IT infrastructure and establishing frameworks for responsible use. Emphasising efficiency, personalisation, and robust support will help overcome barriers and maximise AI chatbots’ potential in education.

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

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.

Developing a Logistic Regression Machine Learning Model that Predicts Viral Load Outcomes for Children Living with HIV in Gutu District, Zimbabwe Ndlovu, Belinda; Kiwa, Fungai Jacqueline; Muduva, Martin; Chipfumbu, Colletor T.; Marambi, Sheltar
Indonesian Journal of Innovation and Applied Sciences (IJIAS) Vol. 5 No. 3 (2025): October-January
Publisher : CV. Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47540/ijias.v5i3.2275

HIV remains a major public health issue globally, particularly in poor resource settings such as the Gutu district of Zimbabwe. The study aimed to develop a predictive viral load outcome model for HIV children based on the CRISP-DM research process. Secondary clinical data for children aged 0–17 years in Gutu were retrieved from the Demographic Health Information System (DHIS2). The study identified age, adherence status, gender, and geographical location as correlated with viral load outcomes. A supervised machine learning logistic regression model was trained with data balance and proper management of complexities. Grid search-based hyperparameter tuning was performed to improve model performance further. The evaluation metrics were accuracy, sensitivity, F1 Score, and area under the receiver operating characteristic curve (AUC-ROC). The model’s performance resulted in 89% accuracy, with all the metrics showing a strong performance. A confusion matrix was used to visualize the results. The findings add to the knowledge on viral load outcome prediction and HIV care in Zimbabwe. The findings suggest that early diagnosis and targeted interventions can improve viral load outcomes in children in Gutu. For future research, the development of the model will be based on more representative data sets and applied to other settings to determine differences in other regions and understand the dynamics of HIV care in children.

The Rise of Quantum Computing and Its Impact on Cybersecurity Vareta, Passmore; Muzenda, Hillary; Nyamupaguma, Tanyaradzwa; Dube, Yangekile; Ndlovu, Belinda
The Indonesian Journal of Computer Science Vol. 14 No. 6 (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.v14i6.5040

As technology continues to evolve, cybersecurity measures tend to be vulnerable to the computational power of quantum computers. These computers perform calculations faster than classical computers. This ability to solve tasks within polynomial time threatens current cybersecurity practices through Shor's and Grover's algorithms. Classical computers rely on mathematical hardness assumptions and are vulnerable to quantum attacks. This paper scrutinizes the double effects of quantum computing on cybersecurity and its ability to support post-quantum resistant technologies. A systematic literature review (SLR) of 24 peer-reviewed articles (2021-2025) obtained from IEEE Xplore, SpringerLink, ACM, and Google Scholar was conducted, and the results identified three integral themes. Firstly, 80% of quantum computing threats studies analysed prove that Shor's algorithm can efficiently factorise large integers, rendering Rivest Shamir Alderman and Elliptic Curve Cryptography obsolete. Secondly, 65% of the studies show that Post-Quantum Cryptography (PQC) offers quantum-resilience in the foreseeable future. In comparison, 25% of Quantum Key Distribution (QKD) papers show practical barriers like signal loss and standardization delays. 15% of studies reveal the urgent need for regulatory and ethical concerns. Key results highlight the urgent need for hybrid cryptographic systems that combine quantum key distribution and post-quantum cryptography, as proposed by 40% of recent publications. 46% of studies show that Europe leads quantum cybersecurity research, driven by collaborative policy efforts. This study suggests practical recommendations for accelerated adoption of NIST-standardised PQC algorithms, investment in QKD infrastructure for critical sectors, and multidisciplinary collaboration to address technical, legal, and ethical gaps. This paper provides a roadmap for mitigating quantum threats and leveraging quantum technologies to transform cybersecurity resilience in the digital era.

Title

Found 9 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 9 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 9 Documents
Search