Machine Learning 5.0 In-depth Analysis Trends in Classification

Dianda Rifaldi (Department of Informatics, Universitas Ahmad Dahlan)
Tri Stiyo Famuji (Department of Information Technology, Universitas Harapan Bangsa)
Setiawan Ardi Wijaya (Department of Information System, Universitas Muhammadiyah Riau)
Ahmed Jaber Abougarair (Electrical and Electronics Engineering, University of Tripoli)
Phichitphon Chotikunnan (College of Biomedical Engineering, Rangsit University)
Alfian Ma'arif (Department of Electrical Engineering, Universitas Ahmad Dahlan)
Furizal (Department of Research and Development, Peneliti Teknologi Teknik Indonesia)

Scientific Journal of Computer Science
Vol. 1 No. 1 (2025): January

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.