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Menampilkan 1–8 dari 8 artikel
Explainable Artificial Intelligence Framework for Interpretable Fault Diagnosis and Remaining Useful Life Prediction in Smart Industrial Rotating Machinery
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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Adaptive Human Robot Collaboration Model Using Computer Vision and Intelligent Control for Flexible Manufacturing Workstations
Deny Prasetyo
; Suyahman Suyahman
; Hadi Jayusman
; Samsinar Samsinar
; Nimas Ratna Sari
; Mursalim Mursalim
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 2
, No 4
(2025)
The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.Thi...
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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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Design and Evaluation of Federated Deep Learning Framework for Privacy Preserving Healthcare Data Analytics Across Heterogeneous IoT Networks
Simon Simarmata
; Panser karo-karo
; Rino Ferdian Surakusumah
; Ahmad Budi Trisnawan
; Suyahman Suyahman
; Bentar Priyopradono
International Journal of Computer Technology and Science
Vol 1
, No 2
(2024)
The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction thr...
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Edge Computing Enabled Real Time Anomaly Detection Framework for Secure Industrial Cyber Physical Systems Using Lightweight Deep Neural Networks
Mursalim Mursalim
; Deny Prasetyo
; Suyahman Suyahman
; Rosalina Yani Widiastuti
; Mursalim Mursalim
; Antoni Pribadi
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 1
, No 1
(2024)
Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by propo...
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Framework for Circular-Economy Based Remanufacturing in Electrical and Electronic Equipment: A Case Study Approach
Abdul Azis
; Edwar Ali
; Erlita Sulistiati
; Rosalina Yani Widiastuti
; Abdurrahman Abdurrahman
; Suyahman Suyahman
International Journal of Engineering and Applied Science
Vol 1
, No 1
(2024)
E-waste has become a critical global issue due to the rapid growth of electronic product consumption and the environmental risks associated with improper disposal. Traditional disposal methods, such as landfilling and incineration, are no longer sustainable as they lead to environmental degradation, health hazards, and loss of valuable resources. In contrast, remanufacturing, a key component of the circular economy, offers a more sustainable solution. This study explores the effectiveness of rem...
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IMPLEMENTASI PEMBELAJARAN PROBLEM BASED LEARNING BERBASIS NILAI PANCASILA DALAM MENGHADAPI REVOLUSI INDUSTRI 4.0 BAGI SISWA KELAS VIII DI SMP NEGERI 1 KARTASURA TAHUN PELAJARAN 2018-2019
Jurnal Global Citizen : Jurnal Ilmiah Kajian Pendidikan Kewarganegaraan
Vol 7
, No 1
(2019)
Penelitian ini bertujuan mendeskripsikan pembelajaran problem based learning berbasis nilaipancasila dalam menghadapi revolusi industri 4.0 bagi siswa Kelas VIII di SMP Negeri 1Kartasura tahun Pelajaran 2018-2019. Penelitian ini adalah penelitian deskriptif kualitatif. Subjekpenelitiannya: guru PPKn dan siswa kelas VIII di SMP Negeri 1 Kartasura, dan objeknya adalahpembelajaran inovatif berbasis nilai pancasila dan refolusi industri 4.0. Metode pengumpulandata menggunakan: observasi, wawancara d...
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