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All Journal International Transactions on Artificial Intelligence (ITALIC)
Astuti, Eka Dian
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Computer Science & IT Control & Systems Engineering Library & Information Science

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Design and Implementation of a Relational Database for an Academic Information System Azuddin, Muna; Yusup, Muhamad; Setiyowati, Harlis; Wibowo, Shesilia; Suwita, Jaka; Basuki, Sucipto; Astuti, Eka Dian
International Transactions on Artificial Intelligence Vol. 3 No. 2 (2025): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v3i2.801

Abstract

This paper focuses on the design and implementation of a relational database for an Academic Information System (AIS), aiming to streamline data management and improve the efficiency of academic processes. The study highlights challenges faced by educational institutions in managing large volumes of student and academic data, often resulting in inefficiencies and errors. The objective is to create a relational database supporting student information, course registrations, faculty assignments, and academic records. The methodology includes developing an Entity-Relationship (ER) model, applying database normalization, and implementing the system using Structured Query Language (SQL). The result is a functional database that improves data retrieval speed, enhances integrity, and simplifies access for academic staff and administrators. The solution contributes to optimizing academic data management by ensuring consistency, reducing errors, and offering scalability for future growth. This research also includes system performance evaluations and stakeholder feedback from faculty, staff, and students. Findings reveal significant improvements in usability, accuracy, and system responsiveness compared to prior legacy systems. Integrated security measures, including role-based access and encryption, safeguard data and ensure compliance with institutional privacy policies. The relational database framework supports real-time access, centralized control, and efficient administrative workflows. Overall, this system strengthens digital infrastructure in educational institutions and aligns with broader digital transformation goals. It enhances data-driven decision-making and supports sustainable, scalable, and secure academic information management, making it a valuable contribution to improving operational performance and educational service delivery in higher education settings.
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.
Co-Authors Afiyanto, Rafid Azuddin, Muna Basuki, Sucipto Harlis Setiyowati Jaka Suwita Kamal, Abdullah Arif Muhamad Yusup Santoso, Nuke Puji Lestari Wibowo, Shesilia