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Reliability Assessment of Attendance Systems Based on Face Recognition Under Varying Lighting Conditions Afiyanto, Rafid; Astuti, Eka Dian; Kamal, Abdullah Arif; Santoso, Nuke Puji Lestari
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.924

Abstract

The rapid adoption of face recognition technology for attendance systems has raised concerns about its reliability under varying lighting conditions, which often affect real world deployment. This study aims to analyze the reliability of a face recognition based attendance system under diverse lighting scenarios, addressing challenges in accuracy and robustness. The research employs a deep learning approach, utilizing a Convolutional Neural Network (CNN) trained on a dataset of facial images captured under controlled and uncontrolled lighting conditions, ranging from low to high illumination levels. The methodology includes preprocessing techniques for illumination normalization and feature extraction, followed by performance evaluation using metrics such as accuracy, precision, and false acceptance rate. Experimental results demonstrate that the proposed system achieves an accuracy of 92% in optimal lighting but drops to 78% under low light conditions, highlighting the impact of illumination on recognition performance. The integration of adaptive preprocessing techniques improves reliability by 12% in challenging scenarios. This study concludes that while face recognition based attendance systems are highly effective, their reliability in diverse lighting conditions can be significantly enhanced through advanced preprocessing and robust algorithm design, offering practical implications for real time biometric applications in dynamic educational and workplace settings.
Leveraging Big Data Analytics to Strategically Expand Digital Microcredit Access for MSMEs Rizky, Agung; Ramaditya, Muhammad; Kamal, Abdullah Arif
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1325

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a pivotal role in driving economic development and job creation, especially in emerging economies. However, limited access to formal credit remains a persistent challenge due to the reliance on conventional financial assessments that often exclude MSMEs with informal or incomplete financial histories. This study aims to investigate how big data analytics can be effectively leveraged to strategically expand digital microcredit access for MSMEs, offering more inclusive and accurate credit evaluation models. The research adopts a qualitative descriptive methodology, incorporating a comprehensive literature review and multiple case studies of fintech platforms that utilize alternative data sources such as e commerce transactions, mobile phone activity, utility bill payments, and social media engagement to construct alternative credit scoring systems. The findings indicate that big data enables improved risk profiling, faster loan processing, and wider financial inclusion by reaching unbanked and underbanked MSMEs. Additionally, the integration of machine learning algorithms in analyzing real time behavioral data enhances decision making precision and operational efficiency in digital lending. However, the study also raises critical issues regarding data privacy, ethical use, and transparency in automated credit decisions. In conclusion, the use of big data analytics offers transformative potential to reshape digital microcredit services, empowering MSMEs through accessible, scalable, and intelligent financial solutions that align with broader goals of sustainable economic inclusion and digital transformation.
Cyber Threats to Press Freedom Resulting from Media Account Hijacking Arribathi, Abdul Hamid; Kamal, Abdullah Arif; Ardien, Annisa
Technomedia Journal Vol 10 No 3 (2026): February
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v10i3.2563

Abstract

The development of digital journalism has expanded the reach of information distribution, while simultaneously increasing the vulnerability of media organizations and journalists to cyber threats that may undermine press freedom. The hacking case involving Narasi.tv demonstrates that media account hijacking is not merely an individual digital crime, but a form of systemic violence and intimidation that affects journalistic integrity and public trust. This study aims to analyze the impact of media account hijacking on press freedom and to develop an applicable technical mitigation framework for digital media organizations. The research adopts a qualitative approach using a case study of the Narasi.tv hacking incident, supported by document analysis, reports from journalist organizations, and a review of the literature on cybersecurity and digital journalism. The findings indicate that collective cyberattacks generate significant psychological and operational impacts and function as a mechanism to suppress critical journalism. This study proposes a layered mitigation framework consisting of preventive technical mitigation, newsroom operational mitigation, and public communication mitigation. Theoretically, this research extends the study of cyber threats from an individual level vulnerability toward an institutional and systemic framework. From a managerial perspective, the findings emphasize that cybersecurity must be positioned as a strategic agenda in digital media transformation. This study is also aligned with the Sustainable Development Goals, particularly SDG 16, SDG 9, and SDG 4, in supporting the development of a secure, sustainable, and trustworthy digital media ecosystem.