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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 72 Documents
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Search results for , issue "Vol 13, No 1: February 2024" : 72 Documents clear
Evaluation of domain sulfur industry for DIA translator using bilingual evaluation understudy method Mohammed Lateef, Huda; Muter Awaad, Ahmad; Ali Hameed, Diadeen; Thiab Hasa, Ghanim; Ameen Faisal, Tahseen
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.4489

Abstract

Evaluation is important part of our system development cycle; it also contributes to improving new machine translation (MT) technology optimum via comparing them with the traditional systems available to determine the weaknesses and the effectiveness to be improved in the proposed MT system. This work aiming to make a study that evaluate the performance and effectivness of the domain sulfur industry (DSI) for English-Arabic DIA translator quality. The recent study has conducted evaluating by making a comparison between this programme with the prominent Google translator through applying a rendering of 1,200 English sentences in bilingual evaluation understudy (BLUE) method. The obtain results show that the efficiency of Google translator is about 30.325%, while DIA translator efficiency in domain sulfur industry is about 73.325% and it’s more effective and give a better translation accuracy. The BLUE method efficiency is about (90.478%) compared with the human expert evaluator.
Software defined networking for internet of things: review, techniques, challenges, and future directions Al-Shareeda, Mahmood A.; Abdullah Alsadhan, Abeer; H. Qasim, Hamzah; Manickam, Selvakumar
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6386

Abstract

Security networks as one of the biggest issue for network managers with the exponential growth of devices connected to the internet. Keeping a big and diverse network running smoothly and securely is no easy feat. With this in mind, emerging technologies like software defined networking (SDN) and internet of things (IoT) hold considerable promise for information service innovation in the cloud and big data era. Therefore, this paper describes the model of SDN and the architecture of IoT. Then this review does not only review the research studies in SDN-IoT but also provides an explanation of the SDN-IoT solution in terms of architecture, main consideration, model, and the implementation of SDN controllers for IoT. Finally, this review discusses the challenges and future directions. This paper can be used as a starting point for thinking about how to improve SDN-IoT security and privacy.
Pre-processing technique of Aquilaria species from Malaysia for four different qualities Mohd Huzir, Siti Mariatul Hazwa; Hasnu Al-Hadi, Anis Hazirah 'Izzati; Zaidi, Amir Hussairi; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Haron, Mohamad Hushni; Almisreb, Ali Abd; Taib, Mohd Nasir
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5577

Abstract

The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.
Evaluation of structural failure resistance of glass fiber reinforced concrete beams Getachew Chikol, Yilachew; Assegie, Tsehay Admassu; Mohmmad, Shaimaa Hadi; Salau, Ayodeji Olalekan; Yanhui, Liu; Braide, Sepiribo Lucky
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6620

Abstract

Glass fiber reinforced concrete (GFRC) is a composite material that is widely used in construction due to its high strength and durability. GFRC is made by adding glass fibers to the concrete mix, which increases the tensile strength of the material. This paper evlautes the shear resistance (SR) of sliced glass fiber (30 mm) GFRC beams. The shear resistance of GFRC beams can be significantly improved by adding glass fibers to the concrete mix. However, further research is needed to fully understand the shear behavior of GFRC and to optimize its design for maximum shear resistance. The result indicates that shear fracture glass fiber is a better alternative for increasing a shear resistance input mechanism.
Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study Mohd Yusof, Najiha ‘Izzaty; Sophian, Ali; Mohd Zaki, Hasan Firdaus; Bawono, Ali Aryo; Embong, Abd Halim; Ashraf, Arselan
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6317

Abstract

Road defect inspection is a crucial task in maintaining a good transportation infrastructure as road surface distress can impact user’s comfortability, reduce the lifetime of vehicles’ parts, and cause road casualties. In recent years, machine learning has been adapted widely in various fields, including object detection, thanks to its superior performance and the availability of high computing power which is generally needed for its model training. Many works have reported using machine-learning-based object detection algorithms to detect defects, such as cracks in buildings and roads. In this work, YOLOv5, YOLOv6 and YOLOv7 models have been implemented and trained using a custom dataset of road cracks and potholes and their performances have been evaluated and compared. Experiments on the dataset show that YOLOv7 has the highest performance with mAP@0.5 score of 79.0% and an inference speed of 0.47 m for 255 test images.
Performance analysis of convolutional neural network architectures over wireless capsule endoscopy dataset Kaur, Parminder; Kumar, Rakesh
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5858

Abstract

Wireless capsule endoscopy is one of the diagnostic methods used to record the video of the gastrointestinal tract. The endoscopy capsule stays in the digestive system for at least eight hours. It is difficult for gastroenterologists to examine such a lengthy video and identify the ailment. Convolutional neural networks (CNN) are a powerful solution to several computer vision problems. CNN can speed up the reviewing time of the recorded video by classifying video frames into various categories. The primary emphasis of this research paper is to examine and evaluate the performance of three different CNN architectures-VGG, inception, and MobileNet-in classifying the disease. Experimental results demonstrate that MobileNetV2’s accuracy is 91%, whereas InceptionV3 and VGG16 have an accuracy of 94% which is better than the accuracy of MobileNetV3. However, MobileNeV2 performed relatively better than the other CNN models in terms of computational time and cost. The model’s F-score, precision, and recall values are computed and compared also.
Multi-objective optimization of distributed energy resources based microgrid using random forest model Vaish, Jayati; Tiwari, Anil Kumar; Kaimal, Seethalekshmi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.7087

Abstract

Microgrids (MG) in integration with distributed energy resources (DERs) are one of the key models for resolving the current energy problem by offering sustainable and clean electricity. This research presents a novel approach to address the complex challenges of optimizing a DERs based microgrid while considering multiple objectives. In this paper, the utilization of a popular machine learning algorithm, random forest (RF) model is proposed to optimize the DERs based MG configuration. The research commences by collecting historical data on energy consumption, renewable energy production, electricity prices, weather conditions, and other relevant factors of Bengaluru City (Karnataka, India) for different seasons. This research covers the conflicting objectives by finding optimal seasonal sizing of the battery, minimum generation cost, and reduction in battery charging cost. The optimization and analysis are done using an ensemble learning-based RF model. The findings from the RF model are compared with meta-heuristics and artificial intelligence (AI) methods such as particle swarm optimization (PSO) and artificial neural networks (ANN) for different seasons, i.e., winter, spring and autumn, summer, and monsoon.
A systematic literature review for smart hydroponic system Muhasin, Haifaa Jassim; Gheni, Ali Yahya; Ismarau Tajuddin, Nur Ilyana; Izni, Nor Aziyatul; Yah Jusoh, Yusmadi; Azhar Aziz, Khairi
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.4738

Abstract

Hydroponics is the cultivation of plants by utilizing water without using soil which emphasizes the fulfillment of the nutritional needs of plants. This research has introduced smart hydroponic system that enables regular monitoring of every aspect to maintain the pH values, water, temperature, and soil. Nevertheless, there is a lack of knowledge that can systematically represent the current research. The proposed study suggests a systematic literature review of smart hydroponics system to overcome this limitation. This systematic literature review will assist practitioners draw on existing literature and propose new solutions based on available knowledge in the smart hydroponic system. The outcomes of this paper can assist future researchers by providing a guideline for user in highlighting approaches for the successful implementation of smart hydroponic system.
Trust aware angle based secure routing approach for wireless sensor network Patil, Hemavati; Tegampure, Vishwanath
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5811

Abstract

Security in wireless sensor network (WSN) is an important approach in the present context as data breaching is becoming more. The data to be routed from source to destination needs more security as WSN has no specific security approach by default. This paper proposes trust based security in WSN using approach. The secure line is drawn from head node to its cluster end point called as angle to provide the security to the nodes which are transferring the data to the head node. Secure line becomes the trust worth line where mobile agent migrates to all the corresponding nodes which are along or near to the secure lines, collects the data and encrypt them. Finally, the data is sent to sink node from head node using a secure path. The agent paradigm is responsible for creating the angle from head node to cluster boundary. Multiple angles can be created if numbers of nodes are more and deployed at different locations. The result shows that the security provided is much better to combat the intruder involvement to breach data along with better network lifetime and minimum delay than compare to conventional techniques.
The effect of FeNi-AlN layer thickness on the response of magnetic SAW sensor by FEM simulation Phu, Do Duy; Hoang, Hong Si; Van Vinh, Le
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6312

Abstract

In this study, we used simulation to investigate the optimal working point of a surface acoustic wave-magnetostriction sensor by varying the thickness of the magnetic sensitive layer using the finite elements method. We evaluated the sensor’s sensitivity by simulating the responses at the optimal point and changing the thickness of the magnetic sensitive layer (h3). Additionally, we reduced the piezoelectric substrate thickness (h1) at the optimal point to determine the limit point of the center frequency (f0) and improve the sensor sensitivity for low magnetic field intensity measurements by performing a wavelength reduction (λ). For the simulation, we selected a delay-line FeNi/IDT/AlN structure with specific materials and electrode parameters. Our results show that the optimal structure of the sensor is at h1=400 μm, λ=40 μm, and h3=1,060 nm, with a maximum f0 of 140.38493 MHz and maximum surface acoustic wave velocity of 5,615.4 m/s. At this optimal structure, the sensitivity reaches the maximum value of 10.287 kHz/Oe with a working range from 0 to 89 Oe. We also found that reducing the piezoelectric substrate thickness to 35 μm significantly reduces the manufacturing and simulation time, but the frequency response cannot determine the center frequency.

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