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All Journal Prosiding Amal Insani Foundation Fusion : Journal of Research in Engineering, Technology and Applied Sciences
M. Adhit Dwi Yuda
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

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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.
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 Aliyah Aliyah Asro Asro Iwan Iwan