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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 75 Documents
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Search results for , issue "Vol 13, No 4: August 2024" : 75 Documents clear
An efficient intrusion detection systems in fog computing using forward selection and BiLSTM Abu Zwayed, Fadi; Anbar, Mohammed; Manickam, Selvakumar; Sanjalawe, Yousef; Alrababah, Hamza; Hasbullah, Iznan H.; Almi’ani, Noor
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7143

Abstract

Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.
Fifth generation core: the performance enhancement of virtual private server and bare metal Putri, Hasanah; Hikmaturokhman, Alfin; Ahmad, Izanoordina; Anwar, Radial; Akbar, Rafli
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6889

Abstract

The fifth generation (5G) architecture represents the most recent advancement in mobile networks and is presently operational in various global places. Several new use cases and applications have been introduced, with a specific focus on improving throughput, reducing latency, minimising packet loss, optimising CPU usage, and maximising memory utilisation. In order to effectively address each scenario, it is necessary to integrate the most advanced technology, putting in significant effort to optimise resources and ensure system adaptability. This strategy will establish an architecture capable of accommodating many scenarios of a shared physical infrastructure by using techniques such as virtualization and cloud-based service deployment. Therefore, in this study, a test was carried out related to the performance of the 5G core network (CN) on bare metal servers and virtual private servers (VPSs). The quality of service (QoS) using Wireshark and Iperf3 is tested by utilizing ‘cpustat’ and free tools. The results of performance comparisons of these two methods on the 5G CN shows throughput values of ≥10 Gbps ≤20 Gbps, latency values of ≤4 ms, and packet loss values of 0%, in accordance with IMT 2020 standards. Thus, the ideal 5G CN services can be realized.
Comparative harmonic elimination techniques for supraharmonic reduction in microgrid Siva, Ayyar Subramaniya; Ramesh Kumar, Sakunthala Ganesan; Dhayalini, Karuppiah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7094

Abstract

In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems with electric vehicle (EV) charging stations, this research compares several harmonic elimination approaches. The increasing deployment of EVs has led to the integration of EV charging stations within MG systems, presents challenges in maintaining a high power quality (PQ). Voltage distortions and SH emissions are caused due to non-linear loads and the intermittent nature of EV charging, which have an effect on the performance and dependability of the MG. In order to solve these problems, multilevel converters (MLCs) are used to produce high-quality waveforms. MLCs use harmonic elimination methods to cut down on SH emissions, which improves the PQ overall. Sinusoidal pulse width modulation (PWM), selective harmonic elimination (SHE), space vector modulation (SVM), and random-PWM (RPWM) techniques are among the harmonic elimination methods compared and analyzed. The results will enable the selection of the most appropriate strategy for minimizing voltage distortion and SH emission in MG systems, while providing valuable insights into the effectiveness of each method.
Citrus leaf disease detection through deep learning approach Islam, Sk. Fahmida; Chakrabarty, Nayan; Uddin, Mohammad Shorif
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.4521

Abstract

The majority of people in the world directly or indirectly depend on agriculture. Plant diseases are a significant threat to agricultural production and food security. Due to its high nutritional value, citrus fruit is one of the most abundant fruits in the world. However, different diseases are responsible for degraded citrus production as well as financial losses to the farmers. Traditionally, visual observation by experts has been attended to diagnose plant diseases. Usually, plant leaf disease recognition methods mainly rely on expert experiences to manually extract the colour, composition, and other features of diseased leaf images. Black spot, greening, canker, and melanoses are four common citrus leaf diseases. Rapid and accurate diagnosis of these diseases is a demand of time. Deep learning is a promising solution to these problems. There are different types of deep learning architecture like ImageNet, GoogleNet, VGG16, ResNet50, and InceptionV3, which show promising results in different object detection. Though most of these benchmark models give almost similar accuracy. However, this paper uses two deep learning models to find the better ones for the detection of citrus leaf disease detection. Hence, InceptionV3 outperforms VGG16 in terms of accuracy.
Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration Kusuma, Purba Daru; Hasibuan, Faisal Candrasyah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6507

Abstract

This study offers a new swarm-based metaheuristic: random-guided optimizer (RGO). RGO has novel mechanics in shifting the random motion into a guided motion strategy during the iteration. In RGO, the iteration is divided into three equal size phases. In the first phase, the unit walks randomly inside the search space to tackle the local optimal problem earlier. In the second phase, each unit uses a unit selected randomly among the population as a reference in conducting the guided motion. In the third phase, each unit conducts guided motion toward or surpasses the best unit. Through simulation, RGO successfully finds the acceptable solution for 23 benchmark functions. Moreover, RGO successfully finds the global optimal solution for four functions: Branin, Goldstein-Price, Six Hump Camel, and Schwefel 2.22. RGO also outperforms slime mold algorithm (SMA), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO) in solving 12, 20, 12, and 1 function consecutively. In the future, improvement can be made by transforming RGO into solid multiple-phase strategy without losing its identity as a metaheuristic with multiple strategy in every iteration.
Virtual teaching and learning for autistic students amidst the pandemic: a systematic literature review Ab Mahadi, Mudrikah; Yahya, Norziana; Akma Ahmad, Nahdatul; Ahmad, Ruzita; Mohd Yusof, Ernie Mazuin
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7487

Abstract

Teaching and learning for autistic students during the COVID-19 pandemic pose challenges for educators. This systematic literature review (SLR) aimed to explore the effectiveness of virtual teaching and learning (VTL) by employing the reporting standards for systematic evidence syntheses (ROSES) framework. Articles from databases like Scopus, Web of Science, and Google Scholar were systematically examined, focusing on themes such as support, coping strategies, teaching methods, flexibility, and communication. The review identified 14 sub-themes within these categories, providing tailored coping and teaching strategies for parents, teachers, and caregivers working with autistic students. From 706 initially identified articles, 376 were selected, with 17 specifically relevant to virtual teaching for autistic students during the pandemic. These findings contribute insights to the existing literature and offer practical implications to enhance VTL experiences for autistic students facing pandemic challenges.
Optimizing EV charging stations: a simulation-based approach to performance and grid integration Sanchez Diaz, William Fabián; Vargas, Jonatan Tolosa; Martinez, Fredy
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.8027

Abstract

This study addresses the optimization of electric vehicle (EV) charging stations, focusing on enhancing performance and grid integration through a comprehensive simulation approach. By employing advanced simulation tools in Simulink® and MATLAB®, alongside electrical installation planning with SIMARIS®, we meticulously analyze the charging process, infrastructure requirements, and their implications on the power grid. Our results demonstrate significant improvements in charging station efficiency and reliability, highlighting the effectiveness of our proposed control strategies and harmonic mitigation techniques. Notably, the integration of renewable energy sources emerges as a pivotal factor in reducing operational costs and carbon emissions, furthering the sustainability of EV charging solutions. The research delineates the environmental benefits, emphasizing the reduction of greenhouse gas emissions and enhancement of urban air quality, pivotal in the global shift towards cleaner transportation modes. This work contributes valuable insights into the design and grid integration of EV charging stations, offering a scalable model for future infrastructure development. It serves as a critical resource for engineers, policymakers, and stakeholders in the realm of electric mobility, advocating for a strategic transition to EVs supported by robust and efficient charging infrastructure.
Effective capacity analysis of configurable intelligent surface-assisted NOMA communications systems Q. Tran, Huu; Van Khuong, Ho
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7485

Abstract

This paper investigates the integration of configurable intelligent surfaces (CIS) into relay radio networks, focusing on communication system enhancement. Towards this end, we propose CIS-assisted non-orthogonal multiple access (NOMA) communication systems to improve direct connections between a base station and two destination nodes. Our primary objective is to assess the net-work’s overall capacity, considering critical factors like signal-to-noise ratio, the number and placement of CIS components, quality of service exponent, and power distribution coefficients. Analytical equations developed in this research closely align with simulation results, validating our theoretical analysis. This study underscores the growing significance of CISs in modern communication systems, introducing adaptability and optimization to wireless networks. By exploring CIS-assisted NOMA communication systems, we contribute to dis-cussions about the evolving landscape of wireless communication technologies, poised to revolutionize information transmission and reception in the digital age.
Recent trend and future prospect in optimization of electric vehicle charging: a systematic review Fauzi, Muhammad Ridha; Zakri, Azriyenni Azhari; Syafii, Syafii
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.8017

Abstract

Electric vehicles charging (EVs) must be done optimally to minimize the impact it causes. EVs are being recognized as a potential way to decrease greenhouse gas emissions and combat climate change. However, there are still difficulties in optimizing these systems to minimize operating costs and EVs charging waiting times. This study investigates several industrial, commercial and residential charging stations. The primary objective of this study is to systematically review the existing literature on optimizing EV charging. The collection of data was centered on scholarly articles released between the years 2018 and 2023 from Scopus, IEEE Xplore. This study presents a systematic literature review of optimizing EVs charging. As a result, 43 EVs charging optimization studies were obtained which were investigated and studied further. Identify and analysis the selected studies, there are two research topics and trends most frequently addressed by researchers: scheduling and coordination. The four most applied methods in EVs charging are identified: particle swarm optimization (PSO), genetic algorithm (GA), linear programming (LP) method, and evolutionary algorithms (EA). Future research directions: develop advanced optimization algorithms, investigating the integration of renewable energy sources into the charging infrastructure, exploring the potential of vehicle-to-grid (V2G) services, studying the impact of EVs charging on the power grid and developing strategies, considering the optimization of charging schedules and coordination strategies for large-scale EVs fleets.
Robust hybrid control strategy for active power management in Kabertene wind farm within Algeria’s PIAT grid Abderrazak, Tadjeddine Ali; Iliace, Arbaoui; Hichem, Hamiani; Mohamed Sofiane, Bendelhoum; Ridha Ilyas, Bendjillali
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7270

Abstract

The paper introduces a hybrid control strategy for optimised active power management in Algeria's Kabertene wind farm, crucial for the pole insalah-adrar-timimoune (PIAT) grid's stability. This strategy merges simultaneous interconnection and damping assignment (SIDA) passivity theory, passivity-based control (PBC), and multivariable proportional-integral-derivative (PID) controllers. This combined approach ensures frequency and voltage stability within the PIAT grid, which encompasses various elements like wind farms, solar plants, gas turbines, and dynamic impedance (Z), current (I), and active power (P) (D-ZIP), load model. By tailoring controllers for doubly fed induction generators (DFIGs) using SIDA-PBC principles and optimising internal parameters, the strategy achieves precise control of active power output. Additionally, particle swarm optimisation (PSO) refines power scheduling, which is especially beneficial for intermittent renewable sources like DFIGs. This comprehensive strategy offers numerous advantages: improved network stability, minimized voltage deviations, reduced frequency fluctuations, and enhanced integration of renewable energy sources. The paper emphasises practical implementation considerations, providing valuable guidance for efficient Kabertene wind farm operation and integration. This research contributes significantly to fostering cleaner and more reliable energy systems, facilitating the PIAT grid's transition towards sustainable energy generation.

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