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All Journal ADI Journal on Recent Innovation (AJRI) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Artificial Intelligence (ITALIC)
Wibowo, Shesilia
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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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.
Orange Technology for Humanistic Innovation in Higher Education Wibowo, Shesilia; Widjaya, Irene Apriani; Zanubiya, Jihan; Evans, Richard; Rahardja, Untung
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 2 (2026): March
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i2.899

Abstract

Amidst the rapid adoption of technology in education, a crucial challenge arises regarding the risk of dehumanizing learning. This study examines Orange Technology as a humanistic innovation approach that seeks to balance digital advancement with human values. Using a qualitative descriptive approach, this research analyzes literature from academic journals and technology education reports, which are then evaluated through a SWOT framework. The analysis results indicate that while Orange Technology holds significant potential to enhance students’ mental well being, digital empathy, and emotional engagement, its implementation faces significant challenges, including limited human resources and inadequate ethical regulations. Therefore, it is concluded that the success of this implementation requires a holistic strategy encompassing investment in human resource training, policy development, and interdisciplinary collaboration. This innovation model has strategic relevance to the Sustainable Development Goals (SDGs), particularly Goal 4 (Quality Education). By focusing on character development and mental well-being, this research contributes to creating an education system that is not only efficient but also inclusive, equitable, and relevant to the holistic needs of future generations.
Machine Learning Approaches for Cybersecurity in Distributed Cloud Infrastructures Prayitno, Dzovani Sandy Putra; Wibowo, Shesilia; Widjaya, Irene Apriani; Martono, Aris; Nanle, Zeze
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1417

Abstract

Rapid cloud adoption has transformed enterprise IT infrastructures, but also introduces complex cybersecurity challenges due to the distributed and dynamic nature of cloud environments, increasing exposure to sophisticated cyber threats. This study aims to design and evaluate machine learning-based approaches to enhance cybersecurity in distributed cloud infrastructures, focusing on improving threat detection accuracy, scalability, and operational efficiency in multi-cloud environments. The proposed method employs a layered machine learning framework integrating supervised and unsupervised algorithms to detect intrusions, anomalous behaviors, and policy violations across distributed cloud nodes, supported by real-time data collection and adaptive model training. A methodological illustration indicates that machine learning approaches can achieve higher detection accuracy approximately 90% compared to traditional rule based systems approximately 78%, while reducing false-positive rates from around 22% to 10%, and experimental results further confirm improved detection performance, reduced false positives, and faster response times while maintaining scalability under increasing workloads. These findings demon- strate that machine learning-driven cybersecurity solutions provide a more adaptive, scalable, and effective defense mechanism, supporting secure and sustainable digital transformation in modern cloud environments.
Co-Authors Astuti, Eka Dian Azuddin, Muna Basuki, Sucipto Evans, Richard Harlis Setiyowati Jaka Suwita Martono, Aris Muhamad Yusup Nanle, Zeze Prayitno, Dzovani Sandy Putra Untung Rahardja Widjaya, Irene Apriani Zanubiya, Jihan