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All Journal Toplama INOVTEK Polbeng - Seri Informatika Journal of Applied Computer and Information Technology
Nur Suhaili Mansor
Universiti Utara Malaysia
Humanities Computer Science & IT Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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HOT TOPICS AND RESEARCH TRENDS IN THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN SMART MANUFACTURING Liu Yonggang; Hapini Awang; Nur Suhaili Mansor; Huda Ibrahim
TOPLAMA Vol. 3 No. 3 (2026): TOPLAMA
Publisher : PT Altin Riset Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61397/tla.v3i3.538

Abstract

With the advancement of Industry 4.0, artificial intelligence that integrates Internet of Things (IoT), big data (BD), edge computing (EC), digital twins, haptic feedback, and other technologies is penetrating various aspects of smart manufacturing, such as predictive maintenance, material design, automatic fault detection, and so on. Based on sampling data extracted from Scopus database between 1986 and 2025, this study employs a bibliometric analysis method to systematically analyse research trends, knowledge structures, and future directions in the field of artificial intelligence and smart manufacturing. The results show that this cross-disciplinary research between artificial intelligence and smart manufacturing has witnessed significant upward growth in recent years, especially since 2017. The top 10 most cited documents, most related documents, some productive authors, and high-frequency keywords have been identified. This study not only helps researchers comprehensively grasp the development trajectory but also provides directional guidance and knowledge map support for subsequent research.
A CONCEPTUAL FRAMEWORK FOR AI SELF-HEALING FOR BIAS MITIGATION: A PROACTIVE ARCHITECTURAL PROPOSAL Harianja Harianja; Elgamar Syam; Alawiyah Abd Wahab; Huda Ibrahim; Hapini Awang; Nur Suhaili Mansor; Adi Permana Sidik
TOPLAMA Vol. 3 No. 2 (2026): TOPLAMA
Publisher : PT Altin Riset Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61397/tla.v3i2.509

Abstract

As the adoption of Artificial Intelligence (AI) continues to expand across various sectors, the issue of bias in training data has emerged as a significant ethical and technical challenge. AI systems are commonly trained using large-scale datasets collected from digital environments such as the internet, social media, and public databases. These datasets often contain historical inequalities, stereotypes, and unbalanced representations of certain demographic groups. Consequently, AI models may unintentionally replicate and amplify these biases in their predictions or decisions. This situation becomes particularly concerning when AI is used in high-stakes domains such as recruitment, healthcare, financial services, and public policy. Most existing bias mitigation strategies rely on reactive approaches, such as adjusting model outputs or modifying datasets after bias has already been identified. While these methods can reduce certain forms of discrimination, they often require significant manual intervention and may not effectively address bias in dynamic data environments. This research proposes a conceptual framework for an AI self-healing system designed to autonomously detect and correct bias in training data before it influences model outcomes. The proposed framework integrates four key modules: Data Monitoring, Bias Analysis, Automated Bias Correction, and a Feedback Loop and Validation mechanism. Together, these components create a continuous workflow that allows the system to identify bias patterns, apply corrective strategies, and verify fairness before data is used for model training. This framework offers a proactive and sustainable approach to bias mitigation while supporting the development of more ethical, robust, and accountable AI systems.
MOBILE SECURITY PRACTICES AMONG MALAYSIAN CITIZENS: A SURVEY OF RISK AWARENESS AND PROTECTIVE BEHAVIORS QIN QIN; Nur Suhaili Mansor; Hapini Awang; Huda Ibrahim; Adi Permana Sidik
TOPLAMA Vol. 3 No. 2 (2026): TOPLAMA
Publisher : PT Altin Riset Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61397/tla.v3i2.512

Abstract

Mobile phones have become an important part of Malaysian youth's daily lives, and as easy as it is to get our hands on a mobile, the growing issues that follow raise alarms for cybersecurity. WordPress site. Although there has been a recent influx of online scam and privacy breach reports, little still exists on how ordinary users perceive mobile security, or if their behaviors are consistent with good risk mitigation practices. This study aims to examine the relationships that exist among mobile security confidence, user practices, and incident experiences with the gender of Malaysian youth. Using an eight-sectioned structured survey as an evaluation instrument, key findings are mainly gleaned from users who report medium levels of confidence in their mobile malware knowledge and overall adoption of protective behaviors such as keeping software updated, only using trusted sources to download apps, and using biometrics. A total of 38.1% of respondents, meanwhile, admitted to previous security incidents largely borne out of the presence of outdated systems and a lack of oversight elements in place for vetting third-party applications. in a rapidly digitizing society. Yet without habitual practice and supportive system design, awareness alone may not be enough.
Navigating Deceptive Realities: Public Perceptions and Cybersecurity Threats of Deepfake Technology Nur Anis Shafiqah Mazlan; Hapini Awang; Nur Suhaili Mansor; Mohamad Fadli Zolkipli; Bingxin Jin
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 3 (2026): August
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/fqwbw793

Abstract

The rapid advancement of deepfake technology presents a profound cybersecurity threat by seamlessly fabricating synthetic media, which severely erodes digital trust. This study aims to evaluate the community's awareness of deepfake threats and assess the necessity of multifaceted mitigation strategies. Employing a quantitative methodology, an online survey was conducted with 67 respondents, predominantly young adults, to measure their exposure, psychological vulnerability, and perspectives on cybersecurity countermeasures. The findings reveal that while the public is generally aware of deepfakes, 75 per cent struggle to visually differentiate manipulated content from authentic media. Consequently, the proliferation of deepfakes has diminished perceived societal trust in online information. Notably, there is a unanimous consensus among respondents demanding strict legal frameworks and comprehensive public education to combat this menace. The study concludes that relying exclusively on technical detection algorithms is insufficient. Instead, preserving information integrity requires a multidisciplinary approach combining robust technological defences, proactive policymaking, and widespread digital literacy trainings.
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.
Co-Authors Adi Permana Sidik Alawiyah Abd Wahab Azlini Awang Bingxin Jin Elgamar Syam Hapini Awang Harianja Harianja Huda Ibrahim Liu Yonggang Mohamad Fadli Zolkipli Nasrul Effendy Mat Nasir Nur Anis Shafiqah Mazlan QIN QIN