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Menampilkan 1–6 dari 6 artikel
A Fault Diagnosis and Intelligent Monitoring Framework Using Explainable Artificial Intelligence for Smart Industrial Machinery
Siska Nar
; Ahmad Nugroho
; Ahmad Subhan Yazid
; Helmi Wibowo
; Alyauma Hajjah
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 2
, No 4
(2025)
Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integr...
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Investigating Longitudinal Effects of Adaptive Digital Learning Ecosystems on Self Regulated Learning and Academic Persistence
Helmi Wibowo
; Benny Daniawan
; Erna Auparay
International Journal of Educational Technology and Society
Vol 2
, No 4
(2025)
This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning...
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Integrated Digital Twin and Physics Informed Machine Learning Model for Real Time Performance Prediction of Industrial Mechanical Systems
Irlon Irlon
; Siti Shofiah
; Helmi Wibowo
; Erick Fernando
; Genrawan Hoendarto
; Mursalim Mursalim
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 2
, No 2
(2025)
Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system b...
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Smart Composite Materials with Embedded Sensors for Structural Health Monitoring in High Performance Mechanical Engineering Applications
Danang Danang
; Riza Phahlevi Marwanto
; Helmi Wibowo
; Muhammad Akbar Hariyono
; Yuanita Sinatrya
International Journal of Industrial Innovation and Mechanical Engineering
Vol 1
, No 2
(2025)
Background: Structural Health Monitoring plays a critical role in ensuring the safety, reliability, and sustainability of high performance composite structures used in aerospace, civil infrastructure, and mechanical systems. Conventional externally mounted sensors often face challenges related to environmental interference, maintenance complexity, and long term stability. Objective: This study aims to develop and validate an integrated smart composite monitoring system with embedded sensing capa...
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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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AI-Based Vision Inspection System for Automated Defect Detection in Additive Manufacturing Processes Using Deep Learning and Transfer Learning Approaches
Dani Sasmoko
; Krisna Widi Nugraha
; Rian Ardianto
; Rosyid Ridlo Al-Hakim
; Helmi Wibowo
; Rinna Rachmatika
International Journal of Industrial Innovation and Mechanical Engineering
Vol 1
, No 2
(2024)
Background: Additive manufacturing (AM) requires reliable and efficient defect detection mechanisms to ensure structural integrity and product quality, yet conventional inspection approaches remain time-consuming and often unsuitable for real-time industrial deployment. Objective: This study aims to develop and experimentally validate an artificial intelligence based vision inspection system capable of accurately detecting surface defects in AM components. Methods: A Convolutional Neural Network...
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