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Menampilkan 1–5 dari 5 artikel
Explainable Artificial Intelligence Framework for Interpretable Fault Diagnosis and Remaining Useful Life Prediction in Smart Industrial Rotating Machinery
Suyahman Suyahman
; Deny Prasetyo
; Ahmad Budi Trisnawan
; Ardy Wicaksono
; Muhamad Furqon
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 1
, No 1
(2026)
Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Var...
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Design of an Edge Computing Based Industrial Internet of Things Architecture for Real Time Predictive Maintenance in Advanced Manufacturing Systems
Simon Simarmata
; Panser Karo-Karo
; Budi Artono
; Muhammad Akbar Hariyono
; Ardy Wicaksono
; Antoni Pribadi
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 2
, No 4
(2025)
Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design...
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Carbon Neutral Industrial Process Optimization through Hybrid Machine Learning and Real Time Energy Efficiency Monitoring Framework
Suyahman Suyahman
; Ardy Wicaksono
; Dwi Utari Iswavigra
; Yogiek Indra Kurniawan
; Very Dwi Setiawan
; Dedi Setiadi
International Journal of Engineering and Applied Science
Vol 2
, No 2
(2025)
Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving envi...
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Digital Twin Driven Real Time Performance Optimization of Smart Factory Production Systems Using Edge Computing and Industrial Internet of Things Architecture
Suyahman Suyahman
; Dwi Utari Iswavigra
; Helmi Wibowo
; Ahmad Budi Trisnawan
; Ardy Wicaksono
; Dwi Atmodjo WP
International Journal of Industrial Innovation and Mechanical Engineering
Vol 1
, No 2
(2024)
Background: The rapid advancement of Industry 4.0 has accelerated the integration of digital technologies such as the Industrial Internet of Things (IIoT), edge computing, and Digital Twin systems in smart manufacturing environments. However, many existing implementations remain fragmented and heavily dependent on centralized cloud infrastructures, resulting in latency constraints, limited scalability, and suboptimal real-time decision making. Objective: This study aims to develop and validate a...
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Explainable Artificial Intelligence Techniques for Enhancing Interpretability and Trustworthiness in Autonomous Vehicle Decision Making Systems
Ahmad Jurnaidi Wahidin
; Siti Shofiah
; Siska Narulita
; Deny Prasetyo
; Ardy Wicaksono
; Teguh Arifianto
; Muhamad Furqon
International Journal of Computer Technology and Science
Vol 1
, No 2
(2024)
Autonomous vehicles (AVs) are revolutionizing transportation by relying on advanced AI techniques like deep learning and reinforcement learning for decision-making and navigation. However, concerns about the opacity of traditional AI models in safety-critical applications such as autonomous driving raise issues related to safety, accountability, and trust. This study explores the integration of Explainable AI (XAI) techniques in AV systems to enhance transparency and interpretability while maint...
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