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International Journal of Electrical Engineering, Mathematics and Computer Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481910     EISSN : 30481945     DOI : 10.62951
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The scope of the this Journal covers the fields of Electrical Engineering, Mathematics and Computer Science. This journal is a means of publication and a place to share research and development work in the field of technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 25 Documents
 1   2   3 
Blockchain-Enhanced Multi-Factor Authentication for Securing IIoT Allyson Eddy; Bram Zoe anak Guillan; Einstein Kent Elias; Eldren Aniell; Shircrayson bin Simon; Muhammad Faisal
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 3 (2024): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i3.16

Abstract

This paper proposes Blockchain-Enhanced Multi-Factor Authentication (BEMFA) to address the limitations of existing authentication mechanisms in the Industrial Internet of Things (IIoT). BEMFA combines multi-factor authentication (MFA) with blockchain technology to ensure robust, scalable, and tamper-resistant security tailored to IIoT environments. This method dynamically manages roles and permissions, detects malicious devices, and ensures data integrity and authenticity. Our results demonstrate that BEMFA significantly enhances security, addressing critical access control challenges and mitigating risks posed by malicious devices while maintaining data integrity.
Enhancing Aspects of IIoT Networks with Federated Learning Blockchain Integrated Authentication Solution Ling, Fang Ting; Ng Hui Wen; Tsi Shi Ping; Vivian Bong Chiaw Cin; Yew Wei Yi; Muhammad Faisal
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 3 (2024): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i3.17

Abstract

The Industrial Internet of Things (IIoT) faces various challenges in ensuring secure communication, authentication, and data integrity due to its distributed nature and evolving threat landscape. To address these issues, this paper proposes the integration of blockchain authentication as a robust solution to enhance security and reliability in IIoT networks. By leveraging Federated Learning with blockchain technology, the proposed solution aims to improve authentication mechanisms by training models across multiple edge devices, increasing fault tolerance, and adaptability while reducing the risk of single points of failure. The use of blockchain technology ensures a tamper-proof and transparent ledger for securely storing authentication data and model updates, enhancing security and integrity in IIoT networks. The results and analysis demonstrate that the integration of Federated Learning and blockchain technology effectively addresses interoperability issues, performance optimization concerns, and security vulnerabilities within IIoT networks, offering a more efficient, secure, and scalable authentication alternative.
Enhancing Security in Industrial IoT Through Blockchain-based Authentication Mechanisms Phiang, Jun Kong; Vivian Yong Siew Yee; bin Hilmi, Hafizuddin; Dedree Leonna Lai; Ng Ee Zoe; Muhammad Faisal
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 3 (2024): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i3.20

Abstract

The Industrial Internet of Things (IIoT) has revolutionized industrial processes, offering automation and data-driven decision-making. However, this interconnectedness brings new security challenges, especially in crucial infrastructure sectors. Traditional security measures are inadequate, leading to the exploration of innovative solutions. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature. This paper proposes a Hybrid Blockchain-Based Authentication Mechanism for IIoT, combining Delegated Proof of Stake (DPoS) and Elliptic Curve Cryptography (ECC). The hybrid architecture utilizes public and private blockchains to ensure scalability, efficiency, and security. Lightweight consensus algorithms, DPoS, are incorporated to optimize performance, while ECC provides efficient cryptographic techniques suitable for IIoT environments. An interoperable framework facilitates seamless integration with existing infrastructure, ensuring regulatory compliance and compatibility. Decentralized identity management further enhances security and privacy. Results and analysis demonstrate the effectiveness of the proposed solution, positioning hybrid blockchain architecture as the most suitable approach for enhancing security in IIoT environments.
Planning for a Microhydro Power Plant (PLTMH) in Raja Jaya Village, Penukal District, Penukal Abab Lematang Ilir Regency Dimas Kasmoro; Emidiana Emidiana; Yudi Irwansi; M.Saleh Al amin
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 3 (2024): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i3.43

Abstract

The need for electrical energy is increasing, especially in rural areas, so building an environmentally friendly energy power plant such as a micro hydro generator can be used to produce electrical energy. This PLTMH planning aims to help the community produce environmentally friendly electrical energy and overcome frequent power outages. The research discusses determining the type of turbine, water discharge, turbine power and generator power at (PLTMH). The water discharge (Q) value of 2.67 m3/sec is the water discharge during the rainy season. From the calculation results, it can generate 25.65 kW of electrical energy, the type of turbine used is an open flume propeller type turbine, the planning of a micro-hydro power plant with a turbine power capacity of 60 kW and a generator of 50 kW greatly influences the power that will be produced by the turbine, which This means that the greater the water discharge, the greater the power generated by the turbine, conversely, if the water discharge is small, the power produced by the turbine is relatively small.
A Comparative Analysis of Deep Learning Models for Predicting Power System Failures Dimas Aditya; Devina Putri; Nanda Asyifa
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 1 (2024): March : International Journal of Electrical Engineering, Mathematics and Comput
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i1.68

Abstract

Power systems are critical infrastructure that face significant challenges due to increasing demand and inherent complexity. Predicting failures in power systems is crucial for enhancing grid reliability, minimizing downtime, and optimizing maintenance processes. This study evaluates various deep learning models, specifically convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models, for predicting power system failures. By analyzing these models’ performance metrics on historical power grid data, the study provides insights into the strengths and weaknesses of each approach. The findings contribute to the development of more robust predictive models for power system reliability.
Mathematical Modeling of Wireless Sensor Networks for Optimized Energy Consumption Maria Teresa Garcia; Jose Antonio Reyes; Ana Patricia Cruz; Carlos Manuel Ramos
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 1 (2024): March : International Journal of Electrical Engineering, Mathematics and Comput
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i1.69

Abstract

Energy efficiency is a critical concern in wireless sensor networks (WSNs) due to the limited power resources available in sensor nodes. Prolonging network lifespan while ensuring reliable data transmission is essential for successful deployment in various applications, such as environmental monitoring, military operations, and industrial automation. This paper presents a mathematical model designed to optimize energy consumption across various nodes in WSNs. By implementing simulations and analyzing data from these models, the study demonstrates significant improvements in extending network lifespan while maintaining reliable data throughput. The findings contribute valuable insights into energy management for large-scale sensor deployments.
Implementing Blockchain Technology for Securing IoT-Based Smart Grids Desi Mutiara Azizah; Caca Oktavia
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 1 (2024): March : International Journal of Electrical Engineering, Mathematics and Comput
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i1.70

Abstract

Smart grids incorporate IoT devices that enhance energy management, monitoring, and overall grid efficiency. However, this interconnectivity also increases vulnerability to cybersecurity threats, posing risks to critical infrastructure. This research investigates the implementation of blockchain technology to secure data transactions within IoT-based smart grids. By leveraging blockchain's decentralized, tamper-resistant characteristics, the study demonstrates improvements in data integrity and cybersecurity for smart grids, providing a potential framework for resilient and secure energy infrastructures.
Nonlinear Control Techniques for Enhanced Stability in Renewable Energy Systems Muhammad Abdullah Khan; Usman Tariq Chaudhry
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 1 (2024): March : International Journal of Electrical Engineering, Mathematics and Comput
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i1.71

Abstract

Renewable energy systems, particularly those incorporating solar and wind power, are prone to stability issues due to the intermittent and fluctuating nature of these energy sources. This paper investigates several nonlinear control techniques—namely, adaptive control, sliding mode control, and fuzzy logic control—that are designed to improve stability in renewable energy systems. Through simulations, we demonstrate how these methods can effectively reduce fluctuations and maintain stable energy output, offering a robust approach to enhancing the reliability of renewable energy systems.
Application of Machine Learning Algorithms for High-Accuracy Image Segmentation in Medical Imaging Fatima Ibrahim Al-Saad; Mohammed Abdullah Al-Hakim
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 1 (2024): March : International Journal of Electrical Engineering, Mathematics and Comput
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i1.72

Abstract

Accurate image segmentation is a pivotal process in medical imaging, essential for supporting diagnosis, treatment planning, and monitoring disease progression. This study evaluates the effectiveness of machine learning algorithms, including U-Net, Fully Convolutional Networks (FCNs), and Mask R-CNN, in achieving high-precision segmentation of medical images. Experimental results demonstrate that these models significantly enhance segmentation accuracy, enabling more precise diagnostic outcomes in clinical settings and advancing the development of automated medical imaging technologies.
Analyzing the Efficiency of Battery Storage Systems in Renewable Energy Grids Fatima Khalid Al-Rashid; Omar Youssef Al-Hassan; Layla Mahmoud Al-Zain
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 2 (2024): June : International Journal of Electrical Engineering, Mathematics and Compute
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i2.73

Abstract

Battery storage systems are essential for stabilizing renewable energy grids, especially for sources like solar and wind power that are inherently variable. This study evaluates various battery technologies and their effectiveness in storing and redistributing energy within renewable energy grids. Through simulations and analysis of performance metrics, the study offers insights into optimal operating conditions for different battery types, highlighting their role in enabling sustainable and stable energy distribution.

Page 1 of 3 | Total Record : 25

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