Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal Jurnal Transformatika Journal Of Human And Education (JAHE) Jurnal Teknik Informatika dan Teknologi Informasi Journal of Technology Informatics and Engineering
Supriadi, Candra
Unknown Affiliation
Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Technology Informatics and Engineering

 1 
Decentralized AI on The Edge: Implementing Federated Learning for Predictive Maintenance in Industrial IoT Systems Supriadi, Candra; Wahyudi , Wiwid; Priyadi, Agus; Jin, Kim So
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.281

Abstract

The integration of Artificial Intelligence (AI) into Industrial Internet of Things (IIoT) systems has enhanced predictive maintenance strategies by enabling early detection of faults in machinery. However, centralized AI models often face challenges related to data privacy, latency, and communication overhead in industrial environments. This study aims to develop a decentralized AI framework utilizing Federated Learning (FL) on edge devices to enhance predictive maintenance in a medium-scale manufacturing plant. The proposed system enables local edge nodes to collaboratively train machine learning models without sharing raw data, thereby preserving data privacy and reducing network load. A prototype was developed using embedded edge devices integrated with vibration and temperature sensors to detect machine anomalies. Federated averaging was used to aggregate local models into a global model. Experimental results show that the federated model achieved 91.4% accuracy in anomaly detection, comparable to centralized approaches, while significantly reducing data transmission volume by 68%. This research demonstrates the feasibility of deploying federated learning on resource-constrained edge devices for predictive maintenance in IIoT environments. The findings suggest that decentralized AI at the edge can offer efficient, privacy-preserving, and scalable solutions for industrial applications
Co-Authors Abdul Khalim, Muhammad Agus Priyadi Heri Kurniawan, Andreas Imaliya, Tri Irwan Sembiring Jin, Kim So Maya Utami Dewi, Maya Utami Nugroho, Aris Sarwo Nurmana, Ayyub Hamdanu Budi Purnomo, Hidriyanto Dwi Qosidah, Nanik S Siswanto SITI KHOLIFAH Sumaryanto Sumaryanto, Sumaryanto Wahyudi , Wiwid Yuli Fitrianto