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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING IJID (International Journal on Informatics for Development) Journal of Information Systems and Informatics
Belinda Ndlovu
National University of Science and Technology
Computer Science & IT

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Quantum Computing in Molecular Design and Drug Discovery: A Systematic Literature Review Charnelle Razo; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1380

Abstract

This study examines how quantum computing (QC) is being applied to molecular design and drug discovery. This study aims to investigates how QC surpasses classical limitations, focusing on empirical performance in precision, accuracy, and optimisation tasks. Study design use PRISMA 2009 guidelines, 15 empirical studies (2020-2025) were included. Data were extracted on the drug-discovery stage, the algorithm used, evaluation metrics, benefits, and limitations. The findings show QC outperforms classical methods particularly through hybrid quantum–classical models. Thirteen studies reported superior gains, including AUC–ROC values of 0.80–0.95, +30% improvement in drug-likeness (QED), +6% increase in prediction accuracy, and up to 99% accuracy in drug–target interaction tasks. However, noisy intermediate-scale quantum (NISQ) hardware limitations and poor scalability limit real-world deployment, due to noise, and limited qubit counts. Consequently, current performance results are largely simulation-based rather than hardware-validated. In contrast to prior algorithm-centric reviews, this study provides a consolidated empirical synthesis and proposes a hybrid quantum–classical pipeline that maps high-performing algorithms across the drug discovery workflow under NISQ-era constraints. These findings inform pharmaceutical research and development by identifying realistic adoption pathways and the boundaries of current technological readiness.
A Systematic Review of Agentic AI for Threat Detection and Mitigation in 5G Networks Kudzaishe Lawal Chizengwe; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1382

Abstract

Fifth-generation (5G) networks face escalating security challenges driven by decentralised architectures, stringent ultra-low-latency requirements, and rapidly evolving threat landscapes. Agentic Artificial Intelligence (agentic AI) autonomous systems that perceive network conditions, decide on countermeasures, and act in real time offers a promising route toward adaptive defence. This systematic review examines how agentic AI is being applied to detect and mitigate threats within 5G networks. Following PRISMA 2009 guidelines, four databases (IEEE Xplore, ACM Digital Library, SpringerLink, and ScienceDirect) were searched, yielding 22 eligible peer-reviewed studies published between 2020 and 2025, selected for explicit 5G relevance and empirical evaluation. The reviewed evidence clusters into four primary security areas: anomaly detection, DDoS mitigation, network slicing security, and intrusion detection. Across these domains, approaches based on federated learning, deep reinforcement learning, and multi-agent systems generally report stronger detection performance and/or more adaptive response behaviour than conventional, reactive baselines, while supporting privacy-preserving intelligence at the edge. However, key deployment barriers remain: 86% of studies rely on simulation-based validation, scalability beyond 100 nodes is insufficiently characterised, and reported coordination delays (120–180 ms) may conflict with 5G latency constraints in time-critical settings. To consolidate findings, this review proposes a Perception–Decision–Action–Feedback conceptual framework and highlights priorities for real-world validation and deployment-oriented evaluation.
Small Language Models for Drug-Drug Interaction Extraction from Biomedical Text: A Systematic Literature Review Fortunate Mutanda; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1430

Abstract

Drug–drug interaction (DDI) extraction from biomedical text is central to pharmacovigilance but remains challenging in resource-constrained clinical environments. While large language models have shown promise, their computational cost and deployment complexity limit practical adoption. This study systematically reviews the role of small language models (SLMs) for DDI extraction and examines their effectiveness, efficiency, and deployability. A systematic literature review was conducted following PRISMA guidelines, covering empirical studies published between 2020 and 2025 in PubMed, IEEE Xplore, ACM and  SpringerLink. Eligible studies were analysed with respect to model architectures, datasets, evaluation metrics, and deployment considerations. Quality assessment was applied to ensure methodological robustness. The synthesis indicates that SLM-based approaches, including CNN-, LSTM-, and lightweight transformer models, can achieve competitive F1-scores on benchmark DDI datasets while requiring substantially fewer computational resources than large language models. However, performance varies across datasets, and real-world clinical evaluations remain limited. These findings support the feasibility of deploying SLM-based DDI extraction systems in resource-constrained clinical and pharmacovigilance settings and provide a baseline for future benchmarking and comparative research in clinical natural language processing.
Towards Cloud-Based Electronic Health Records in Healthcare Systems: Security, Scalability, and Migration Strategies: A Systematic Literature Review Musawenkosi Moyo; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1431

Abstract

Cloud-based Electronic Health Records (EHRs) are being adopted rapidly worldwide, but implementation still encounters recurring obstacles in security assurance, elastic scalability, and migration readiness. Prior reviews often treat these issues separately, leaving limited practical guidance for organizations planning end-to-end deployment. This study synthesizes recent evidence on cloud EHR adoption by examining how security controls, scalability claims, and migration strategies interact in real implementation contexts. A systematic literature review following PRISMA guidelines was conducted across ACM Digital Library, PubMed, IEEE Xplore, and ScienceDirect, covering peer-reviewed studies published from 2021 to 2025. Results show that the literature is technically mature in proposing encryption, access control, auditing, and performance optimization, and frequently reports scalability advantages. In contrast, evidence on complete migration pathways—data mapping, interoperability, validation, cutover planning, and post-migration assurance—remains sparse, with many studies relying on simulations rather than longitudinal deployments. The review also identifies geographic concentration in high-income settings, limiting generalizability to resource-constrained health systems. By integrating security, scalability, and migration readiness within a socio-technical, implementation-oriented perspective, this review provides actionable directions for secure and scalable cloud EHR transitions.
Post-Quantum Migration in the Financial Sector: A Systematic Review of Readiness, Risks, and Transition Frameworks Hillary Muzenda; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1477

Abstract

Quantum computing threatens the classical cryptographic systems underpinning financial infrastructure, exposing payment platforms, interbank networks, and digital services to Harvest-Now-Decrypt-Later (HNDL) risks. This study systematically reviews quantum threats and post-quantum cryptographic readiness in the financial sector to assess preparedness, identify implementation challenges, and synthesize migration pathways aligned with emerging standards. A PRISMA-guided review of 17 peer-reviewed studies published between 2020 and 2025 was conducted, examining quantum threat models, post-quantum cryptographic schemes, quantum key distribution architectures, and sector-specific deployment barriers. The review finds that lattice-based schemes, especially CRYSTALS-Kyber and CRYSTALS-Dilithium, are the leading candidates for financial adoption, while hybrid cryptographic approaches offer the most feasible transition strategy. However, the current evidence base is predominantly simulation-driven, with limited real-world deployment and validation. The study provides a sector-specific synthesis of quantum threats, post-quantum readiness, and migration pathways in financial systems, and advances an integrated readiness and migration framework based on cross-study thematic analysis.
Sentiment Analysis in Electronic Health Records for Patient-Centric Care: A Systematic Literature Review of Methods, Applications, and Challenges Caroline Mhlanga; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1545

Abstract

This study examines the role of sentiment analysis in EHR narratives for enhancing patient-centred care, focusing on methodological approaches, application domains, and implementation challenges in clinical settings. A systematic literature review (SLR) was conducted in accordance with PRISMA guidelines. Relevant studies were retrieved from Scopus, Web of Science, IEEE Xplore, and PubMed. The search, conducted in September 2025, included peer-reviewed articles published between 2021 and September 2025. The findings reveal a clear shift from rule-based and traditional machine learning approaches to transformer-based models. Sentiment analysis is increasingly applied in areas such as mental health, oncology, and patient experience monitoring. However, most implementations remain domain-specific and are not fully integrated into routine clinical workflows. This study provides a structured synthesis of sentiment analysis in EHRs and identifies key gaps between methodological advancements and real-world implementation. It advances a socio-technical perspective that integrates analytical performance, clinical applicability, and governance considerations, offering a consolidated lens for understanding sentiment-aware healthcare systems. Despite rapid methodological progress, the impact of sentiment analysis in EHRs remains constrained by limited scalability and insufficient integration into clinical practice.
Federated Learning for Privacy-Preserving Sentiment Analysis in Distributed Electronic Health Record Environments: A Systematic Literature Review Frederick Mlungisi Dandure; Belinda Ndlovu
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1546

Abstract

Federated learning (FL) has emerged as a privacy-preserving approach for distributed healthcare analytics, yet its application to sentiment analysis of unstructured electronic health record (EHR) narratives remains limited. This systematic review examined the empirical maturity, methodological trends, and governance implications of federated sentiment-aware learning in distributed EHR settings. Following PRISMA 2020, searches were conducted in IEEE Xplore, Scopus, Web of Science, ScienceDirect, and PubMed on January 5, 2026, covering peer-reviewed studies published from January 2021 to January 2026. After screening and eligibility assessment, 29 empirical implementation studies were included in the qualitative synthesis, while conceptual and survey papers were reviewed contextually but excluded from the core analysis. The evidence shows that FL in healthcare is advancing mainly in structured prediction and privacy-preserving infrastructure. By contrast, sentiment-aware learning on unstructured clinical narratives remains at an early stage, with limited implementation and validation. This review distinguishes empirical from conceptual contributions and proposes a governance-aware, literature-derived framework to guide future implementation-focused research.
Cost-Optimised IoT Architecture for Real-Time E-Waste Monitoring with Operational Validation Belinda Ndlovu; Zvinodashe Revesai; Kudakwashe Maguraushe
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1553

Abstract

Electronic waste (e-waste) is the fastest-growing solid waste stream worldwide, yet formal collection systems remain limited. Many existing Internet of Things (IoT) solutions emphasize advanced functionality at the expense of cost efficiency and practical deployability. This paper presents a cost-optimized IoT architecture for real-time monitoring of e-waste bins. The proposed system adopts a four-layer architecture integrating ESP32 microcontrollers, ultrasonic sensors for fill-level detection, and infrared sensors for monitoring, supported by a Node.js backend that provides real-time data updates. System validation was conducted through sensor calibration (n = 30), functional testing, stress testing, and cost-performance benchmarking against RFID-, GSM-, and LoRa-based alternatives. Experimental results demonstrate a fill-level accuracy of ±3.2%, temperature precision of ±1.8°C, system reliability of 97.3%, uptime of 98.7%, and an average latency of 2.1 s. The deployment cost was USD 78 per bin, which is approximately 40% lower than comparable RFID-based systems. In addition, the system reduced unnecessary collection trips by 35% and yielded an estimated return on investment (ROI) of 8.5 months. These results show that a low-complexity, cost-efficient IoT design can provide a scalable and practical solution for e-waste bin monitoring.
Twitter (X) Sentiment Analysis on Monkeypox: A Systematic Literature Review Hazel Chamboko; Kudakwashe Maguraushe; Belinda Ndlovu
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5196

Abstract

Monkeypox has a risk of growing into a global threat. Understanding public sentiments is crucial for effective emergency responses, as it helps counter misinformation, enhance communication, and improve the retention and application of public health information. This systematic review of literature aims to provide foundations for identifying existing algorithms, commonly used data collection methods, and pre-processing techniques applied to Twitter discussions on Mpox. The review followed the PRISMA guidelines. Relevant literature was retrieved from ScienceDirect, IEEE, PubMed, and Springer databases, resulting in 15 studies that met the inclusion criteria. Most preprocessing methods include stop word removal, lemmatisation, and tokenisation; commonly used data collection methods include Twitter API, Academic API V2, Snscrape, Twint, and Tweepy. Classification of sentiment tended to be hybrid models like CNN-LSTM or transformer-based models such as BERT, which also perform well in dealing with complex linguistic patterns. These recent models, additionally, addressed other very important issues like misinformation detection, irony, and bot-generated content, which earlier models would often fail to tackle. Despite these advancements, much work still needs to be done in improving the accuracy, generalizability, and interpretability of sentiment analysis models in live monitoring of public health.
Early Detection of Diabetic Retinopathy Through Explainable AI Models: A Systematic Review Tinashe Ngwazi; Belinda Ndlovu; Kudakwashe Maguraushe
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5200

Abstract

Diabetes, if not detected early, can lead to serious complications such as vision loss, known as diabetic retinopathy. Explainable Artificial Intelligence (XAI) can enhance traditional Machine Learning methods, which are not understandable and transparent in diagnostic tasks. This Systematic Literature Review explores data inputs that influence the performance of XAI models in detecting diabetic retinopathy, how XAI techniques can enhance early detection outcomes in diabetic retinopathy, the challenges in implementing these techniques and the ethical implications of using these models in clinical practice. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach guided the search in 4 databases, Springer, Science Direct, PubMed and IEEE Xplore. The findings reveal that XAI techniques like Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (GRAD-CAM) offer opportunities like early detection outcomes, integration with existing clinical processes, enhancing trust in AI systems, improving accuracy and personalised treatment. XAI can also facilitate collaboration among clinicians, maintaining fairness in AI systems and supporting adherence to ethical standards. However, research on clinical validation of these models, as well as standardised performance evaluation metrics, is lacking.
Co-Authors Amazing Maphosa Caroline Mhlanga Charnelle Razo Fortunate Mutanda Frederick Mlungisi Dandure Hazel Chamboko Hillary Muzenda Kudakwashe Maguraushe Kudzaishe Lawal Chizengwe Musawenkosi Moyo Pride Dube Robert Selemani Tinashe Ngwazi Zvinodashe Revesai Zvinodashe Revesai