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Case Study Analysis of the Use of Cloud Computing for Assessing Big Data Risks Fadi Fataftah; Bassey Isong
Journal of Information System and Informatics Vol 5 No 2 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i2.478

Risks associated with adopting big data and cloud computing and exposing sensitive information must be evaluated as usage of these technologies continues to rise rapidly within businesses. Also, the company needs to investigate the potential consequences of cyber security threats, considering the severity of those risks. There has been no comparative analysis of the risk assessment methods available to businesses in various nations. Thus, the researcher in this study asked forty people from four countries (Canada, Jordan, South Africa (SA), and the United Kingdom (UK)) questions on the risk assessment procedures at their respective organizations using semi-structured interviews. After compiling and analyzing the data, it became clear that Canada and the UK were the frontrunners in adopting big data and cloud computing. It also demonstrated that Jordan and SA are in the early phases of an evolving adoptive relationship. Recommendations are made to strengthen the organization's standing in light of the different risk assessment frameworks used in each country.

Blockchain-Enabled Vaccination Registration and Verification System in Healthcare Management Bassey Isong; Tshipuke Vhahangwele; Adnan M Abu-Mahfouz
Journal of Information System and Informatics Vol 5 No 2 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i2.497

Client-server-based healthcare systems are unable to manipulate a high data volume, prone to a single failure point, limited scalability, and data integrity. Particularly, several measures introduced to help curb the spread of Covid-19 were not effective and patient records were not adequately managed and maintained. Most vaccination-proof certificates were forged by unauthorized parties and no standard verification medium exists. Therefore, this paper proposes a blockchain-enabled vaccination management system (VMS). VMS utilizes smart contracts to store encrypted patients record, generate vaccination certificates, and verify the legitimacy of the certificate using a QR code. VMS prototype is implemented using Ethereum, a public blockchain and simulations performed based on Apache JMeter and Hyperledger Caliper to assess its performance in terms of throughput, latency and response time, and the average time per transaction. Results show VMS achieved an average: response time of 132.24 ms, the throughput of 379.89 tps, latency of 204.60 ms, and time of transactions is 10s-12s for 1000 transactions. Also, its comparison with the centralized database shows the traditional database’s effectiveness in transaction processing but lacks data privacy and security strengths. We, therefore, recommend the use of blockchain in the healthcare system and other related sectors such as elections, and student records management to ensure data privacy and security and rid the system of a single point of failure.

The Impact of Team Dynamics on Software Quality and Productivity: Evidence from South Africa Tshipuke Vhahangwele; Bassey Isong; Adnan Abu Mahfouz
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.v8i1.1438

Software quality and productivity are influenced not only by technical practices but also by the social dynamics within development teams. This study investigates the combined effect of team dynamics, including trust, communication, collaboration, diversity, and conflict resolution, and software development practices on project outcomes. A mixed-methods design combined regression, Spearman’s Rho, and thematic analysis of survey data from 124 South African software professionals. The findings indicate that trust is the strongest positive predictor of software quality and productivity, while communication effectiveness and the use of collaboration tools also improve software outcomes. Equally, unstructured collaboration, excessive planning meetings, and poorly managed communication channels negatively affect performance. Diversity and effective conflict resolution were positively associated with productivity and efficiency. Thematic analysis corroborated these findings, illustrating how unclear communication, low trust, and dysfunctional collaboration lead to delays, rework, and lower quality. The study confirms that successful software outcomes emerge from the alignment of social and technical subsystems, highlighting the critical role of team dynamics in realising the full potential of software development practices. It contributes empirical evidence from an understudied developing-country context and proposes a socio-technical framework to enhance software quality and productivity.

A Dependency- and Trust-Aware Task Scheduling Framework for Efficient Internet of Things Edge Systems Fulufhelo Hopewell Mamidza; Bassey Isong
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.v8i2.1489

The rapid growth of the Internet of Things (IoT) has significantly increased the number of connected devices, generating massive volumes of data and placing substantial demands on edge and fog computing infrastructures. Traditional resource management approaches often overlook task dependencies, which can lead to inefficient resource utilization, increased execution delays, reduced reliability, and potential security risks in distributed IoT environments. To address these challenges, this paper proposes an improved dependency-aware task scheduling framework designed to operate between edge devices and edge servers. The framework employs directed acyclic graph (DAG) modeling to represent task dependencies and execution order, trust-aware node selection to avoid malicious, overloaded, or unreliable nodes, and Particle Swarm Optimization (PSO) to support adaptive resource allocation under dynamic and heterogeneous workloads. Experimental results demonstrate that the proposed framework achieves an average latency of 50 ms, throughput of approximately 500 transactions per second (tps), and a task completion rate of 98%. These findings indicate that the proposed approach outperforms conventional scheduling methods by improving latency, throughput, reliability, security, and overall task execution efficiency in IoT-enabled edge computing environments.

AI-Assisted Development Tools and Team Dynamics in South African Software Engineering Teams Bassey Isong; Tshipuke Vhahangwele
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1615

AI-assisted development tools are widely adopted in software engineering (SE), yet their effects on team dynamics and software delivery outcomes remain poorly understood in sub-Saharan African settings. This paper investigates how AI tool integration influences team roles, collaboration, skill requirements, and software delivery outcomes among South African software development professionals. A mixed-methods design was used, combining a structured survey with thematic analysis of open-ended responses from 40 participants across developer, tester, DevOps, and team lead roles. Multiple linear regression and Spearman's rank correlation were applied to quantitative data; thematic analysis followed the six-phase approach of Braun and Clarke. Findings show that GitHub Copilot was used by 75% of respondents. Interpersonal trust was the strongest predictor of development speed (β = 0.485, p = 0.017), exceeding all AI-specific variables in the model. AI use at the adoption onset reduced development speed; frequency of use increased it. Role transformation was reported by 95% of respondents and predicted team productivity. However, causal inference is not warranted given the cross-sectional design and reliance on self-reported measures. The findings are further constrained by a purposive sample of 40 drawn from networked professional communities, which limits statistical power and generalisability. To the authors’ knowledge, no prior published study has examined AI adoption and team dynamics within a South African SE population, using this combination of methods, though a systematic literature review of that literature was beyond the scope of this study.

Transfer Performance and LIME Explanation of Ensemble Classifiers in Cross-Project Defect Prediction Bassey Isong
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1667

Ensemble methods are widely used in cross-project defect prediction (CPDP), particularly in projects that lack sufficient historical data by training on external source projects. However, no prior study has compared Bagging, Boosting, and Stacking directly under a Leave-One-Project-Out (LOPO) protocol or examined whether within-project performance rankings carry over to the cross-project setting. We evaluated three ensemble classifiers on five NASA MDP datasets sharing a common Halstead and McCabe feature schema. SMOTE is applied exclusively to pooled source data to prevent leakage into the target. A no-SMOTE baseline isolates the contribution of source-only SMOTE. LIME explanations are aggregated over thirty instances per model to assess feature importance consistency across the project boundary. Within-project evaluation shows Stacking achieves the highest F1 on four of five datasets, peaking at 0.503 on KC1. Under LOPO, these rankings reverse as Bagging and Boosting transfer more reliably, while Stacking's F1 drops by up to 0.258 points. Source-only SMOTE consistently improves transfer across all targets and ensembles. LIME consistency analysis produces undefined Spearman rank correlations, indicating that thirty-instance aggregation is insufficient to produce stable rank vectors for 21-feature datasets. To the best of our knowledge, this is the first study to compare all three ensemble strategies under LOPO on a shared NASA dataset feature schema. Particularly with a no-SMOTE control, aggregated LIME analysis, and a pilot meta-feature study identifying dataset size as the most actionable label-free predictor of ensemble suitability for CPDP deployment.

Misinformation Detection in Low-Resource Languages and Health Domains: Review and Evaluation Framework Bassey Isong; Rose Linah
The Indonesian Journal of Computer Science Vol. 15 No. 3 (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.v15i3.5129

Misinformation on digital platforms harms public health decisions, electoral processes, and institutional trust. Its detection in low-resource languages (LRLs) remains structurally neglected. No prior survey applies a scoring framework to assess study quality or enable cross-study comparison. This review examines 51 peer-reviewed studies published between 2021 and 2026, following the PRISMA 2020 protocol across four dimensions: detection methodology, dataset coverage, health-domain adaptation, and explainable AI (XAI) integration, and proposes the LRL misinformation evaluation framework (LRLM-EF). The five-criterion evaluation framework was applied retrospectively to all reviewed studies. The findings reveal that dataset construction is the dominant research activity, with new corpora built for Amharic, Bangla, Bengali, Luganda, Sepedi, Sesotho, Xitsonga, isiZulu, Kurdish Sorani, and several Arabic dialects. Transformer-based models outperform classical and deep learning baselines in most settings; classical classifiers achieve comparable results where annotated data is scarce. Health-domain coverage is narrow. Research mostly concentrates on COVID-19 and vaccine misinformation, while HIV, malaria, and reproductive health appear in no reviewed studies. Multimodal fusion improves detection in all five studies where it was tested, yet audio-based detection in any LRL setting is absent. XAI is applied in a few studies, exclusively through post-hoc LIME, with no study evaluating its effect on user decisions. LRLM-EF scoring reveals that most studies address fewer than half the framework criteria, with adversarial evaluation and standardised reporting as the weakest dimensions. However, two contradictions exist in the evidence. Classical retrieval outperforms neural similarity on rare-terminology datasets, and augmentation volume shows no reliable accuracy gain, which further expose absence of a shared benchmarking standard.

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