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Menampilkan 1–5 dari 5 artikel
Multi Objective Evolutionary Optimization of Additive Manufacturing Process Parameters for Enhanced Mechanical Performance and Surface Integrity
Yulaikha Maratullatifah
; Dwi Utari Iswavigra
; Very Dwi Setiawan
; Mursalim Mursalim
; Budi Wibowo
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 1
, No 1
(2026)
Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM op...
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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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Adaptive Reinforcement Learning Driven Intrusion Detection and Response Mechanisms for Zero Trust Architecture in 5G and Beyond Networks
Dwi Utari Iswavigra
; Ahmad Jurnaidi Wahidin
; Yogiek Indra Kurniawan
; Yulaikha Maratullatifah
; Tuti Susilawatii
International Journal of Computer Technology and Science
Vol 1
, No 2
(2024)
This study explores the development and evaluation of an adaptive Intrusion Detection and Response System (IDRS) driven by Reinforcement Learning (RL) for securing 5G networks. The RL-based IDS is designed to overcome the limitations of traditional security systems by dynamically learning from real time network traffic and adapting to emerging cyber threats. Introduction: The rapid growth of 5G networks, with their increased number of connected devices and complex traffic patterns, necessitates...
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Hybrid Reinforcement Learning and Robust Adaptive Control Strategy for Autonomous Manufacturing Systems under Uncertain and Dynamic Production Environments
Irlon Irlon
; Teguh Muryanto
; Sayyid Jamal Al Din
; Dwi Utari Iswavigra
; Yulaikha Maratullatifah
; Very Dwi Setiawan
International Journal of Mechanical, Industrial and Control Systems Engineering
Vol 1
, No 1
(2024)
This study explores the integration of hybrid AI control models, combining reinforcement learning (RL) and robust adaptive control, to improve the adaptability, performance, and stability of autonomous manufacturing systems. Traditional control systems, while effective under stable conditions, often struggle to cope with disturbances and varying production demands. Hybrid AI models, which integrate classical control methods such as Proportional Integral Derivative (PID) with machine learning tec...
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