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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Journal of Applied Data Sciences
Titoni, Erica
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Risk Analysis In Indonesian Educational Online Learning Systems: A Systematic Literature Review Sama, Hendi; Tjahyadi, Surya; Titoni, Erica
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13239

Abstract

Over the past few years, the idea of online learning has gained popularity and due to the COVID-19 pandemic, has become essential everywhere, including in Indonesia. Nevertheless, the deployment of e-learning management systems has brought about several IT hazards that affect academic operations' user experience security and overall operational efficacy worldwide. This study uses a systematic literature review (SLR) of research publications indexed in Google Scholar to examine the possible dangers related to e-learning management systems in academic institutions. The classification, assessment, and identification of the hazards that educational institutions encounter while incorporating e-learning systems into their infrastructure are the main objectives of this study. Issues like operational failures, obsolete hardware and software, cybersecurity threats, network accessibility and stability concerns, data privacy, and illegal access are the main topics of this study. Additionally, this study highlights the necessity of more effective and focused risk mitigation techniques created especially to meet the demands of Indonesian academic settings
Iris Identification Using Resnet Iris Feature Extraction Architecture For Better Biometric Security Sama, Hendi; Tukino, Tukino; Siahaan, Mangapul; Titoni, Erica
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1166

Abstract

Iris recognition is widely acknowledged as one of the most reliable biometric modalities due to its high uniqueness, rich textural patterns, and long-term stability. Unlike other biometric traits, iris characteristics resist forgery, aging effects, and environmental variations, making it suitable for high-security applications. Recently, convolutional neural networks (CNNs) have been extensively applied in iris recognition to improve feature representation and classification accuracy. However, many CNN-based approaches still depend on conventional segmentation and handcrafted features, which reduce robustness under noisy data, illumination variations, occlusions, or unconstrained environments. To address these limitations, this study proposes an enhanced iris identification framework combining a modified T-Net for precise segmentation with deep residual feature extraction for improved discrimination. Unlike conventional systems focus mainly on classification, the proposed approach emphasizes segmentation-driven feature consistency, ensuring extracted features originate from accurately localized iris regions. This design enhances stability and reliability, particularly under challenging imaging conditions. The framework leverages transfer learning and efficient representation learning strategies, enabling high accuracy even with a limited labelled data. Evaluations on three benchmark datasets CASIA-IrisV4, IITD Iris Database, and UBIRIS.v2 covering both controlled and less-constrained acquisition scenarios. Results show that it achieves classification accuracy of up to 98.35%, while maintaining computational efficiency suitable for deployment. The proposed architecture offers a robust, data-efficient, and scalable solution for secure biometric authentication, with strong potential for real-world applications such as access control, identity verification, and high-security authentication systems.
Co-Authors Mungkap Mangapul Siahaan Sama, Hendi Tjahyadi, Surya tukino, tukino