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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Bulletin of Social Informatics Theory and Application Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK) INTER TECH
Alim, Syariful
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences

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Journal : INTER TECH

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Tinjauan Integrasi Teknologi Deep Learning Untuk Revolusi Industri Dalam Sistem Siber-Fisik Zainal, Rifki Fahrial; Alim, Syariful; Arizal, Arif; Purnama, Rangsang
INTER TECH Vol 3 No 1 (2025): INTER TECH
Publisher : Fakultas Teknik Universitas Bhayangkara Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/i.v3i1.1266

Abstract

An important development in industrial automation is the combination of deep learning with cyber-physical systems (CPS), which allows systems to make data-driven, intelligent decisions with little assistance from humans. With an emphasis on its capacity to handle massive amounts of data for tasks including object detection, semantic segmentation, predictive maintenance, and autonomous control, this research investigates the revolutionary effects of deep learning on CPS. It looks at how technology has developed from early frameworks that relied on visual cues to complex systems that use cutting-edge neural networks that can function in dynamic, unstructured contexts. The study also emphasizes how important it is to integrate ethical frameworks, organizational preparedness, and human-centered design in order to successfully implement CPS. This study analyzes important trends, obstacles, and best practices that influence the application of deep learning in CPS through an extensive examination of recent literature. The significance of CPS in facilitating the Industry 4.0 and Industry 5.0 paradigms—which prioritize sustainability, human-machine collaboration, and real-time adaptation in industrial processes—is given particular attention.
Co-Authors Abror, Akhdan Arif Arizal, Arif Hendrata, Ferial Hindarto, Djarot Hozairi Juhairiyah Masdukil Makruf, Masdukil Nurul Hamidah, Mas Rangsang Purnama Rifki Fahrial Zainal