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Journal of Applied Computer and Information Technology
Published by PT. Global Research Innovation
ISSN : -     EISSN : 31250033     DOI : https://doi.org/10.67131/jacoit.v1i1.4
Core Subject :
Journal of Applied Computer and Information Technology (JACoIT) is an international, peer-reviewed journal dedicated to publishing high-quality original research articles, reviews, and case studies in the domains of applied information technology, computer science, and computational systems. The journal provides a scholarly platform for academics, researchers, practitioners, and students to disseminate innovative ideas, empirical findings, and practical applications that integrate theory and practice in computing and information technologies. JACoIT emphasizes the applied, practical, and solution-oriented dimensions of computer and information technologies, particularly those addressing real-world challenges, enabling digital transformation, and advancing technological innovation across diverse sectors and disciplines. Scope The scope of JACoIT encompasses, but is not limited to: - Applied Information Technology - Computer Science and Computational Methods - Information Systems and Software Engineering - Artificial Intelligence, Machine Learning, and Deep Learning - Data Science, Big Data Analytics, and Business Intelligence - Internet of Things (IoT), Cloud Computing, and Edge Computing - Human–Computer Interaction (HCI) and User Experience (UX) - Information and Network Security, Data Privacy, and Cybersecurity - E-Government, E-Commerce, E-Learning, and Digital Services - Computer Networks, Embedded Systems, and Intelligent Devices - Information Management and Decision Support Systems - Emerging and Sustainable Computing Technologies
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Articles 10 Documents
 1 
Revisiting big data governance: Insights from contemporary frameworks and emerging challenges Muhammad Noor; Fauziah Baharom; Haslina Mohd
Journal of Applied Computer and Information Technology Vol. 1 No. 1 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i1.1

Abstract

The rapid expansion of big data ecosystems has intensified demand for robust data governance frameworks that ensure data quality, security, privacy, and the creation of strategic value. Although numerous big data governance frameworks have been proposed over the past decade, they differ substantially in scope, maturity, and applicability, leaving critical gaps in addressing emerging technological, organizational, and ethical challenges. This study revisits the landscape of big data governance frameworks published between 2018 and 2025 through a comprehensive review and thematic synthesis of 13 peer-reviewed studies from high-impact journals and leading conferences. Unlike previous review studies that primarily collect governance dimensions or conceptual components, this study adopts a pattern-oriented analytical perspective to synthesize contemporary frameworks and identify recurring governance across contexts. The analysis identifies four major governance patterns, including fragmented governance approaches across sectors, context-specific frameworks without generalizable foundations, the growing intersection of AI and governance, and the imperative for adaptive and dynamic governance mechanisms. These patterns extend existing knowledge by explaining not only which governance elements are present in current frameworks but also how and why governance practices evolve in response to complex data ecosystems. The findings highlight the necessity of an integrated, adaptive, and context-sensitive big data governance framework that can respond to technological evolution and the complexity of the modern data environment. In addition, this study provides a structured roadmap for future research and offers actionable insight for organizations aiming to strengthen their data governance capabilities in increasingly data-driven environments.
Artificial intelligence and business intelligence in small and medium enterprises: A bibliometric review of emerging research directions Nasrul Effendy Mat Nasir; Hapini Awang; Nur Suhaili Mansor; Azlini Awang
Journal of Applied Computer and Information Technology Vol. 1 No. 1 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i1.4

Abstract

Artificial Intelligence (AI) and Business Intelligence (BI) are advancing decision-making and strategic management in Small and Medium Enterprises, thereby improving competitiveness and digital sustainability. While there is a plethora of research on the application of AI and BI in SMEs, the literature is scattered. It varies in its understanding, frameworks, and methodologies, making it challenging to integrate the available knowledge. Prior reviews lack depth and focus, providing little to no commentary on publication patterns, foundational ideas, and prospective research paths. This work attempts to fill this research gap with a bibliometric analysis of AI and BI in SMEs, based on a sample of publications from Web of Science and Scopus from the period of 2015 to 2025. This analysis aims to address significant gaps in the literature by measuring publication volumes across countries and by authors worldwide, using digital maps, collaboration networks, co-occurring keywords, and co-citation and thematic mapping techniques to monitor the research productivity and intellectual geography of the discipline. The results obtained demonstrate the presence of several significant and nascent research areas, improving our understanding of the essential technological, scientific and strategic research advancements in these fields. This review draws on relevant theory and policy concerning the UN Sustainable Development Goals (SDGs 2030), especially SDG 8 (Decent Work and Economic Growth), and SDG 9 (Industry, Innovation and Infrastructure), which strengthen the value and relevance of this bibliometric analysis in shaping the adoption and sustainability of AI–BI within the context of SMEs.
Evolution, hotspots, and prospects of AI-powered telehealth: A bibliometric study Bingxin Jin; Hapini Awang; Nur Suhaili Mansor
Journal of Applied Computer and Information Technology Vol. 1 No. 1 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i1.5

Abstract

The integration of artificial intelligence (AI) and telehealth has become a key enabler of smart healthcare, improving accessibility and efficiency in medical services. However, comprehensive reviews focusing on the evolution, knowledge structure, and future directions of AI-powered telehealth remain limited. This study addresses this gap by conducting a bibliometric analysis of 427 publications indexed in the Scopus database from 2010 to 2025. The analysis examines publication trends, citation patterns, influential studies, and keyword co-occurrence networks. The findings reveal a two-phase development pattern characterized by “scale expansion” followed by “quality improvement”, with 2020 identified as a critical turning point. The results further highlight three major clusters of influential research focusing on AI-based diagnosis, specialized healthcare applications, and technological integration. In addition, four primary research themes are identified: AI-based telehealth applications, enabling technologies, research methodologies, and precision telehealth. Emerging research directions include the development of mobile health solutions, explainable AI, mixed-method approaches, and improvements in system capacity and reliability. This study provides a comprehensive knowledge framework and offers theoretical and practical insights to support the sustainable and high-quality development of AI-powered telehealth.
Cybersecurity risk management for digital retail: Strategies, frameworks and implementation Nurul Hanna Mohd Saleh; Nur Anis Atiqah Hussin; Maharubiney Suthursan Kumar; Mohamad Fadli Zolkipli
Journal of Applied Computer and Information Technology Vol. 1 No. 1 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i1.6

Abstract

The digital transformation of wholesale, supermarket and e-commerce operations has rendered cybersecurity a cornerstone of business resilience in the modern trading and retail sector. This study examines the critical cybersecurity threats facing this industry through a qualitative risk assessment, identifying phishing, ransomware, supply chain attacks and data breaches as the most prevalent and impactful risks. The analysis underscores that these threats exploit the sector's inherent characteristics: high transaction volumes, reliance on interconnected digital ecosystems and the processing of large quantities of sensitive customer data. In response, the study advocates for a strategic, integrated approach to cybersecurity governance. It proposes that combining the risk-based structure of the NIST Cybersecurity Framework (CSF) with the prescriptive payment security controls of the PCI DSS and the governance rigor of ISO/IEC 27001 provides a comprehensive model for effective risk mitigation. The findings highlight that moving beyond compliance-centric checklists to develop proactive cyber resilience is crucial. This requires strategic investments in foundational controls, robust incident response planning and strict third-party risk management to safeguard operations, ensure regulatory adherence and maintain customer trust in an increasingly hostile digital landscape.
Cybersecurity risk management strategy for AI and SaaS platforms: A NIST framework approach Muhammad Zaim Zainuddin; Muhammad Sharin Yasin; Muhammad Azerul Azaman; Mohamad Fadli Zolkipli
Journal of Applied Computer and Information Technology Vol. 1 No. 1 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i1.9

Abstract

The rapid growth in Information Technology (IT) and Software industries particularly within Artificial Intelligence (AI) and Software-as-a-Service (SaaS) has accelerated the pace of the fourth industrial innovation, and at the same time, this growth produced complex vulnerabilities to security. The conventional defense mechanisms are becoming less effective over time against the evolving threats like adversarial data poisoning, API exploits and advanced ransomware attacks targeting cloud infrastructures. The primary goals of this paper are to address these issues by developing a comprehensive risk management plan that is based on the NIST Cybersecurity Framework (CSF). Additionally, this study identifies critical vulnerabilities in modern AI and SaaS environments using a qualitative risk assessment approach and a likelihood-versus-impact matrix. The analysis shows that data breaches and API exploitation are the most serious threats, which have significant impact on organization operations and the high likelihood. Moreover, the findings indicate that incorporating the NIST CSF core capabilities such as Identify, Protect, Detect, Respond and Recover is a well-organized framework of minimizing these high-priority threats using layered preventive and detective controls. Ultimately, the results highlight how important it is to embrace standards-based systems to shift organizations from reactive security measures to proactive resilience to ensure the integrity and continuity of the interconnected software ecosystem.
A loop or stagnation? Lecturers’ meta-literacy gaps and the 5th Industrial Revolution Mubaraq Ibrahim Abdulsalam; Mutiu Babatunde Ibrahim; Fatimoh Danmaigoro; Ahmed Fahdilat Talatu
Journal of Applied Computer and Information Technology Vol. 1 No. 2 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i2.19

Abstract

The Fifth Industrial Revolution (5IR) has transformed higher education by emphasizing human–technology collaboration, ethical reasoning, and sustainable innovation, creating new demands for lecturers’ meta-literacy skills. However, a gap remains between institutional expectations and lecturers’ preparedness, as many educators experience technological anxiety, resistance, or stagnation amid rapid digital transformation. This study investigated lecturers’ autonomy in integrating emerging technologies, meta-literacy competence, and professional relatedness in sustaining technology-enhanced teaching at universities in Kwara State, Nigeria. Grounded in Self-Determination Theory (SDT), the study explains how the satisfaction of autonomy, competence, and relatedness influences lecturers’ professional growth or stagnation. A descriptive correlational design was employed using a standardized questionnaire administered to 100 university lecturers selected through convenience sampling. Data were analyzed using descriptive statistics and Pearson Product-Moment Correlation. The results showed high levels of autonomy in technology integration (x̄ = 3.05), meta-literacy competence (x̄ = 3.05), and moderately high professional relatedness (x̄ = 2.93). However, shortcomings were identified in access to institutional resources, advanced AI-driven educational technologies, and structured professional learning communities. Correlation analysis revealed significant positive relationships among lecturers’ autonomy, meta-literacy competence, and professional relatedness (p < 0.05), indicating that these factors collectively support sustainable technology-enhanced teaching. Although lecturers demonstrated strong motivation and capability to adopt emerging technologies in the 5IR era, inadequate institutional support may hinder sustained innovation. Strengthening autonomy, competence, and professional relatedness is therefore essential to prevent pedagogical stagnation and foster continuous innovation in higher education.
Penetration testing for software supply chain security: A lifecycle-oriented review of vulnerabilities, third-party dependencies, and mitigation strategies Abdiqani Cusman Abdalla; Yuchong Cui; Ihab Hussein Al Musawi
Journal of Applied Computer and Information Technology Vol. 1 No. 2 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i2.22

Abstract

Modern software systems increasingly rely on open-source components, third-party libraries, cloud services, containers, and automated CI/CD pipelines. This dependence improves development speed but also expands the software supply chain attack surface beyond internally written code. Vulnerabilities may be introduced through transitive dependencies, build scripts, package repositories, artifact storage, or software update channels. This review examines how penetration testing can support software supply chain security by moving beyond passive vulnerability identification toward dynamic validation of exploitable risk. A structured review approach was used to examine recent literature on software supply chain attacks, SBOM adoption, dependency governance, CI/CD security, and security testing. The findings show that penetration testing is most useful when it is mapped to specific lifecycle stages, including dependency selection, development environments, build pipelines, artifact repositories, distribution mechanisms, and monitoring. The review also identifies practical limitations, such as unclear test boundaries, incomplete dependency visibility, limited automation, and resource constraints. The paper contributes a lifecycle-oriented view of penetration testing for software supply chain security and highlights how testing can complement SBOMs, secure development practices, and continuous monitoring.
Implementation of Real-ESRGAN for image resolution enhancement in a YOLOv8-based vehicle license plate identification system under low-light conditions Tri Handayani; Mustazzihim Suhaidi
Journal of Applied Computer and Information Technology Vol. 1 No. 2 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i2.24

Abstract

Vehicle license plate identification systems still face significant challenges under low-light conditions, including low image resolution, high noise levels, and decreased detection accuracy. Conventional methods such as contrast enhancement or filtering are often insufficient to recover textual details of license plates. This study implements Real-ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) for image resolution enhancement prior to license plate detection using YOLOv8. The proposed system is deployed on an NVIDIA Jetson Nano edge computing device to support real-time inference. Low-quality input images acquired under low-light conditions are first enhanced using Real-ESRGAN to restore image details and improve resolution. The enhanced images are then processed by YOLOv8 for license plate detection and character recognition. Experiments were conducted on 1,200 vehicle license plate images captured under three conditions: nighttime, underground parking, and heavy rain. Evaluation results show that Real-ESRGAN improves PSNR by 4.21 dB and SSIM by 0.12 compared with the original low-light images. License plate detection accuracy (mAP@0.5) increases from 71.32% to 94.58%, and character recognition accuracy improves from 65.47% to 91.23%. The average system response time is 1.89 s, which remains within an acceptable range for real-time smart parking applications. Overall, the proposed Real-ESRGAN–YOLOv8 framework effectively improves vehicle license plate identification under low-light conditions.
Beyond the human firewall: A systematic analysis of deepfake-mediated social engineering and the erosion of traditional security awareness Mohd Ruhaifi Zainol; Marhakim Mokhtar; Mohamad Fadli Zolkipli
Journal of Applied Computer and Information Technology Vol. 1 No. 2 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i2.25

Abstract

The proliferation of generative artificial intelligence has transformed the cyber threat landscape, particularly in the domain of social engineering. Deepfake technology, encompassing synthetic audio and video generation, has emerged as a vector for advanced phishing campaigns that can bypass conventional controls, exposing limitations of traditional Security Awareness Training (SAT) when confronted with AI-driven deception. This paper presents a systematic analysis of empirical evidence on deepfake-enabled social engineering and its implications for existing security-awareness frameworks, drawing on 56 primary studies involving 86,155 participants, supplemented by 47 documented corporate incidents and a review of current technical detection methods. Pooled human detection accuracy for deepfake content was 55.54% (95% CI [52.3%, 58.8%]), only marginally above chance; the pooled estimate is, however, accompanied by substantial heterogeneity (I² = 78.4%) and should be interpreted as an average performance rather than a uniform inability to discriminate. Traditional SAT was associated with a non-significant +1.6% improvement in deepfake detection, whereas SAT incorporating synthetic-media examples produced significant gains of +8.1% to +15.5%. The study identifies three persistent limitations in current awareness training: the absence of deepfake-specific detection heuristics, inadequate calibration of trust in response to synthetic authority cues, and insufficient inoculation against cognitive-load manipulation; a 21.1-percentage-point laboratory-to-field performance gap was also observed. The paper proposes a resilience-oriented training framework that integrates technical literacy, psychological preparedness, and organisational verification mechanisms. Rather than declaring the “human firewall” obsolete, the analysis argues for its reconceptualisation as a complementary safeguard within layered, procedure-anchored defence.
Security challenges in serverless architectures: Vulnerabilities and penetration testing approaches for AWS Lambda and Google Cloud Functions Norsyazwani Mohd Puad; Paiwand Hadi Hama Saeed; Braw Araz Mohammed; Mohamad Fadli Zolkipli
Journal of Applied Computer and Information Technology Vol. 1 No. 2 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i2.27

Abstract

This study examines the evolving security landscape of serverless computing, specifically focusing on AWS Lambda and Google Cloud Functions. While serverless architectures offer significant scalability and cost advantages, their event-driven nature introduces unique vulnerabilities that traditional infrastructure-based security measures often fail to address. To identify these risks, a multi-methodological approach was employed, involving systematic literature mapping, platform benchmarking, and threat modeling using the STRIDE framework. The research specifically analyzed the "blast radius" of compromised functions and the efficacy of current penetration testing methodologies. Results indicate that Identity and Access Management (IAM) misconfigurations are the primary driver of cloud breaches, accounting for 42% of critical vulnerabilities, while traditional network scanning yielded zero actionable detection data. Furthermore, simulation data revealed that unthrottled "Denial-of-Wallet" attacks can cause catastrophic financial loss within minutes. Based on these findings, a five-layer defense-in-depth framework is proposed, integrating secure secret management, automated dependency scanning, and identity-centric governance. The study concludes that securing serverless environments requires a paradigm shift from network-level protection to granular application logic validation and continuous observability. These measures are essential for maintaining data integrity in decentralized cloud-native environments.

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