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All Journal Scientific Journal of Computer Science
Dianda Rifaldi
Department of Informatics, Universitas Ahmad Dahlan
Computer Science & IT

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Machine Learning 5.0 In-depth Analysis Trends in Classification Dianda Rifaldi; Tri Stiyo Famuji; Setiawan Ardi Wijaya; Ahmed Jaber Abougarair; Phichitphon Chotikunnan; Alfian Ma'arif; Furizal
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): January
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.18

Abstract

In the era of Technology 5.0 Machine Learning continues to show significant advancements across various sectors. This study aims to examine the latest trends in Machine Learning classification, focusing on four key approaches Explainable Artificial Intelligence, Federated Learning, Transfer Learning, and Generative Adversarial Networks. The methodology involves a comprehensive literature review of research in Asia and experimentation with related datasets. The findings indicate that Explainable Artificial Intelligence enhances transparency and accuracy in data classification, Federated Learning enables decentralized learning while safeguarding data privacy, Transfer Learning improves accuracy with small datasets, and Generative Adversarial Networks aids in data augmentation for better model training. In conclusion, these techniques not only enhance the efficiency and accuracy of classification but also open up new opportunities for innovation in various fields, including healthcare, transportation, and cybersecurity.
Co-Authors Ahmed Jaber Abougarair Alfian Ma'arif Furizal Phichitphon Chotikunnan Setiawan Ardi Wijaya Tri Stiyo Famuji