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Menampilkan 1–3 dari 3 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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Development of a Digital Twin Based Smart Green Building Energy Management Model Integrating IoT Sensors and Predictive Sustainability Analytics
Asro Asro
; Solihin Solihin
; John Chaidir
; Febri Adi Prasetya
; Tuti Susilawati
; Muhamad Furqon
; Bentar Priyopradono
International Journal of Engineering and Applied Science
Vol 2
, No 2
(2025)
Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also...
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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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