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All Journal Prosiding Amal Insani Foundation Fusion : Journal of Research in Engineering, Technology and Applied Sciences
Aliyah Aliyah
Universitas Cendekia Abditama
Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Other

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BRIDGING ACADEMIA AND INDUSTRY: A SYSTEMATIC LITERATURE REVIEW OF SOFTWARE ENGINEERING EDUCATION APPROACHES AND THEIR EFFECTIVENESS Aliyah Aliyah; Asro Asro; Achmad Rozi; M. Adhit Dwi Yuda
Prosiding Amal Insani Foundation Vol. 3 (2026): PROSIDING INTERNASIONAL
Publisher : Amal Insani Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The gap between academia and industry remains a major challenge in software engineering education. Rapid technological change requires graduates to possess not only strong theoretical knowledge but also practical competencies, collaboration skills, and familiarity with real software development practices. This study presents a Systematic Literature Review (SLR) examining educational approaches used to bridge the academia-industry gap in software engineering education. A total of 54 scientific articles published between 2015 and 2024 were reviewed through a structured selection process based on PRISMA principles. The review focuses on curriculum design, project-based learning, agile-based education, industry collaboration, capstone projects, internships, and competency-based assessment. The findings show that project-based learning, agile learning, industry partnerships, and internship programs are the most frequently used approaches to improve students' readiness for professional roles. However, challenges remain in aligning university learning outcomes with rapidly changing industry expectations, particularly regarding practical experience, soft skills, and exposure to real tools and workflows. This article provides recommendations for strengthening software engineering education through adaptive curricula, stronger industry involvement, and continuous competency-based evaluation.
Artificial Intelligence and Machine Learning in Education: A Systematic Literature Review of Transformative Trends and Future Directions Aliyah Aliyah
Fusion : Journal of Research in Engineering, Technology and Applied Sciences Vol. 3 No. 1 (2026): Fusion - April
Publisher : PT. Faaslib Serambi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66341/fusion.v3i1.282

Abstract

The transformation of education in the digital era has been significantly accelerated by the integration of Artificial Intelligence (AI) and Machine Learning (ML), fundamentally reshaping how learning is designed, delivered, and assessed. This study aims to systematically identify emerging trends, key benefits, prevailing challenges, and future directions of AI and ML applications in education through a Systematic Literature Review (SLR) approach. The reviewed literature was sourced from leading academic databases, including Scopus, IEEE Xplore, and ScienceDirect, covering publications from 2015 to 2025.  The findings reveal that AI and ML technologies have been widely implemented in various educational domains, particularly in adaptive learning systems, automated assessment mechanisms, and intelligent virtual assistants that facilitate personalized learning experiences. Despite these advancements, several critical challenges persist, notably digital inequality, data privacy concerns, and the limited technological literacy among educators, which hinder the effective adoption of these technologies. Furthermore, the study highlights that the future of education will increasingly rely on the integration of intelligent systems that enable data-driven, flexible, and learner-centered environments. The insights derived from this SLR are expected to provide valuable guidance for policymakers, educators, and technology developers in formulating adaptive and sustainable educational strategies in the era of artificial intelligence.
Systematic Literature Review Metode Data Science dalam Prediksi Kinerja dan Keamanan Jaringan Cloud Aliyah Aliyah; M. Adhit Dwi Yuda; Iwan Iwan
Fusion : Journal of Research in Engineering, Technology and Applied Sciences Vol. 2 No. 2 (2025): Fusion - Oktober
Publisher : PT. Faaslib Serambi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66341/fusion.v2i2.307

Abstract

Transformasi menuju cloud computing meningkatkan kompleksitas pengelolaan kinerja jaringan dan risiko ancaman keamanan siber, sehingga diperlukan pendekatan prediktif yang akurat dan adaptif. Penelitian ini menyajikan Systematic Literature Review (SLR) mengenai penerapan metode data science dalam prediksi kinerja jaringan dan deteksi ancaman keamanan siber pada lingkungan cloud. Tinjauan dilakukan mengikuti pedoman PRISMA terhadap publikasi periode 2015–2025 yang diindeks pada Scopus, IEEE Xplore, ACM Digital Library, dan ScienceDirect. Hasil kajian menunjukkan bahwa metode machine learning seperti Support Vector Machine dan Random Forest, serta deep learning seperti Convolutional Neural Network dan Long Short-Term Memory, mendominasi penelitian terkait. Teknik anomaly detection dan hybrid learning terbukti efektif dalam mengidentifikasi pola serangan kompleks pada infrastruktur cloud berskala besar. Namun, tantangan utama masih mencakup ketidakseimbangan data, keterbatasan generalisasi model, dan minimnya dataset terbuka. Studi ini memberikan pemetaan tren metodologis dan celah penelitian sebagai dasar pengembangan model prediktif yang lebih robust dan skalabel pada infrastruktur cloud.
Co-Authors Achmad Rozi Asro Asro Iwan Iwan M. Adhit Dwi Yuda