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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 74 Documents
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Search results for , issue "Vol 13, No 3: June 2024" : 74 Documents clear
A discernment of round-robin vs SD-WAN load-balancing performance for campus area network Gamilla, Anazel P.; Tolentino, Anjela C.; Payongayong, Reina T.
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5945

Abstract

Efficient load balancing is crucial for optimizing network performance and ensuring seamless connectivity in modern campus area networks (CANs). With the proliferation of data-intensive applications and the increasing reliance on cloud-based services, organizations are seeking effective load-balancing solutions to distribute network traffic evenly across available resources. The continuous improvement of devices, tools, and techniques to cater a large amount of network traffic, started to be employed on different campuses. Understanding the best approach to maximize the utilization of the network resources is crucial in order to stabilize and maintain the network. The study aims to discern the round-robin and software defined-wide area network (SD-WAN) techniques based on defined metrics and conducted with a predefined payload for commonly used application conditions. The analysis shows that SD-WAN delivers a much superior performance than round-robin based on the criteria. The local area network (LAN) test shows difference between the two types of technology for the three given metrics. The WAN test shows that the round-robin has higher packet loss, latency, and jitter than the SD-WAN technology. While round-robin may suffice for small-scale deployments with relatively homogeneous traffic patterns, SD-WAN offers more sophisticated capabilities for larger CANs with diverse application workloads and distributed locations.
Clutter evalution of unmanned surface vehicles for maritime traffic monitoring Nadiy Zaiaami, Muhammad; Abd Rashid, Nur Emileen; Ismail, Nor Najwa; Ibrahim, Idnin Pasya; Enche Ab Rahim, Siti Amalina; Zalina Zakaria, Nor Ayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6836

Abstract

A traditional maritime radar system is utilized for ship detection and tracking through onshore transmitters and receivers. However, it faces challenges when it comes to detecting small boats. In contrast, unmanned surface vehicles (USVs) have been designed to monitor maritime traffic. They excel in detecting vessels of various sizes and enhance the capabilities and resolution of maritime radar systems. Nevertheless, just like conventional radar systems, USVs encounter difficulties due to environmental interference and clutter, affecting the accuracy of target signal detection. This research proposes a comprehensive numerical assessment to tackle the clutter issue associated with USVs. This involves gathering clutter signal data, performing numerical analysis, and employing distribution fitting techniques that leverage mathematical distributions to unravel data complexity. The root mean square error (RMSE) is applied in this analysis to validate the efficacy of the distribution model. The results of this study aim to formulate a clutter model that can enhance radar performance in detecting small vessels within cluttered environments.
An efficient synthetic minority oversampling technique-based ensemble learning model to detect COVID-19 severity Mishra, Smriti; Kumar, Ranjan; Tiwari, Sanjay K.; Ranjan, Priya
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6774

Abstract

The COVID-19 pandemic has highlighted the importance of accurately predicting disease severity to ensure timely intervention and effective allocation of healthcare resources, which can ultimately improve patient outcomes. This study aims to develop an efficient machine learning (ML) model based on patient demographic and clinical data. It utilizes advanced feature engineering techniques to reduce the dimensionality of dataset and address the issue of highly imbalanced data using synthetic minority oversampling technique (SMOTE). The study employs several ensemble learning models, including XGBoost, Random Forest, AdaBoost, voting ensemble, enhanced-weighted voting ensemble, and stack-based ensembles with support vector machine (SVM) and Gaussian Naïve Bayes as meta-learners, to develop the proposed model. The results indicate that the proposed model outperformed the top-performing models reported in previous studies. It achieved an accuracy of 0.978, sensitivity of 1.0, precision of 0.875, F1-score of 0.934, and receiver operating characteristic area under the curve (ROC-AUC) of 0.965. The study identified several features that significantly correlated with COVID-19 severity, which included respiratory rate (breaths per minute), c-reactive proteins, age, and total leukocyte count (TLC) count. The proposed approach presents a promising method for accurate COVID-19 severity prediction, which may prove valuable in assisting healthcare providers in making informed decisions about patient care.
Secure map-based crypto-stego technique based on mac address Kasasbeh, Dima S.; Al-Ja’afreh, Bushra M.; Anbar, Mohammed; Hasbullah, Iznan H.; Al Khasawneh, Mahmoud
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.7140

Abstract

Steganography and cryptography are spy craft cousins, working differently to achieve the same target. Cryptography is perceptible and observable without understanding the real content, while steganography hides the content so that it is not perceptible or observable and without producing noticeable changes to the carrier image. The challenge is finding the right balance between security and retrievability of embedded data from embedding locations without increasing the required embedded information. This paper proposes a secure map-based steganography technique to enhance the message security level based on the sender and recipient mac addresses. The proposed technique uses rivest-shamir-adleman (RSA) to encrypt the message, then embeds the cipher message in the host image based on the sender and recipient media access control addresses (mac addresses) exclusive or operation "XOR" results without increasing the required embedded information for the embedding location map. The proposed technique is evaluated on various metrics, including peak signal-to-noise ratio (PSNR) and embedding capacity, and the results show that it provides a high level of security and robustness against attacks without an extra location map. The proposed technique can embed more data up to 196.608 KB in the same image with a PSNR higher than 50.58 dB.
IoT-based fertigation system for agriculture Idris, Fakrulradzi; Latiff, Anas Abdul; Buntat, Muhammad Amirul; Lecthmanan, Yogeswaran; Berahim, Zulkarami
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6829

Abstract

Fertigation system has been widely used by farmers to automate some processes of crops productions. A conventional system requires workers to prepare a fertilizer mixture, before transferring it into a main storage tank to be mixed with water. Then, electrical conductivity (EC) of the mixture will be measured. The existing fertigation system still relies heavily on workers and is manually operated and prone to human error. Therefore, internet of things (IoT) based fertigation system has been developed to deliver the fertilizer mixture with consistent EC value automatically to the plants. The main system controller is designed using ESP32 development module. The operation of the system can be monitored using an IoT dashboard and farmers can also control the system remotely. Alert will be given to the farmers if the condition of the system or plant does not meet the predefined settings. The values of EC together with temperature and humidity sensors are recorded for further analysis. A testbed is set up to provide fertigation to 120 polybags eggplants. Using the proposed fertigation system, the eggplants have been harvested earlier, therefore reducing the fertilizer usage. The cost of this IoT based fertigation system is lower compared to existing commercial products.
Accident black spots identification based on association rule mining Mbarek, Abdelilah; Jiber, Mouna; Yahyaouy, Ali; Sabri, Abdelouahed
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6135

Abstract

This paper presents an analytical approach to identifying the important characteristics of accident black spots on Moroccan rural roads. An association rule mining method is applied to extract road spatial characteristics associated with fatal accidents. The weighted severity index was calculated for each section, which was then used to determine the severity levels of black spots. The apriori algorithm is applied to find the correlation between road characteristics and the severity levels of black spots. Then, a general rule selection method is proposed to identify the rules strongly associated with each severity level. The results show that the proposed approach is effective in identifying the most important factors contributing to accidents. Furthermore, it shows that the combination of several road characteristics, such as road width, road surface, and bridge presence, may contribute to fatal accidents. The general rule selection found that wet, bad surfaces, and narrow shoulders were significantly associated with accidents on rural roads. The findings of the present study can help develop effective strategies to reduce road accidents and thus improve road safety in the country.
Autonomous vehicle tracking control for a curved trajectory Hasan, Hasnawiya; Samman, Faizal Arya; Anshar, Muh; Sadjad, Rhiza S.
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6060

Abstract

Recently, research about trajectory tracking of autonomous vehicles has significantly contributed to the development of autonomous vehicle technology, particularly with novel control methods. However, tracking a curved trajectory is still a challenge for autonomous vehicles. This research proposes a state feedback linearization with observer feedback to overcome some difficulties arising from such a path. This approach suits a complex nonlinear system such as an autonomous vehicle. This method also has been compared with the linear-quadratic regulator (LQR) method. So, the goal of this research is to improve the control system performance of autonomous vehicles that are stable enough to navigate a curved path. Moreover, the study shows that the developed control law can track the curved path and solve existing problems. However, improvements are still necessary for the vehicle's performance and robustness.
Cross-project software defect prediction through multiple learning Zakariyau Bala, Yahaya; Abdul Samat, Pathiah; Yatim Sharif, Khaironi; Manshor, Noridayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5258

Abstract

Cross-project defect prediction is a method that predicts defects in one software project by using the historical record of another software project. Due to distribution differences and the weak classifier used to build the prediction model, this method has poor prediction performance. Cross-project defect prediction may perform better if distribution differences are reduced, and an appropriate individual classifier is chosen. However, the prediction performance of individual classifiers may be affected in some way by their weaknesses. As a result, in order to boost the accuracy of cross-project defect prediction predictions, this study proposed a strategy that makes use of multiple classifiers and selects attributes that are similar to one another. The proposed method's efficacy was tested using the Relink and AEEEM datasets in an experiment. The findings of the experiments demonstrated that the proposed method produces superior outcomes. To further validate the method, we employed the Wilcoxon sum rank test at 95% significance level. The approach was found to perform significantly better than the baseline methods.
A new topology of non-isolated AC-DC quadratic boost converter with enhanced power traits Ahmed, Istiak; Azad, Ferdous S.; Hasan, Shameem; Al Mamun, Abdullah; M. Salim, Khosru
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6624

Abstract

A novel AC-DC quadratic boost converter (QBC) topology is presented in this paper which can provide higher power factor (PF), lower total harmonic distortion (THD) and higher voltage when compared to a conventional AC-DC boost convertero This is achieved by using additional switched capacitor and switched inductor in the power processing stage. The presented converter is analyzed theoretically, and a voltage gain equation is derived. A simulation model is created to evaluate the converter performance under various duty cycles, switching frequency, and changing output load. The input PF, THD, and voltage gain of the simulated model were compared with conventional converter to determine the validity of the suggested converter circuit. The results show that the efficiency, PF, THD and voltage gain value reaches approximately 97.9%, 0.98, 16.14, and 2.83 respectively at a duty cycle of 50% with fixed output load of 100 Ω. A dual loop voltage and current controller is also arrayed with the proposed circuit which allows further enhancement in PF (0.997) and THD (7.45%). This converter can be a suitable option for systems where isolation is not necessary but high PF, high voltage gain and low THD are required.
Combining dual attention mechanism and efficient feature aggregation for road and vehicle segmentation from UAV imagery Nguyen, Trung Dung; Pham, Trung Kien; Ha, Chi Kien; Le, Long Ho; Ngo, Thanh Quyen; Nguyen, Hoanh
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6742

Abstract

Unmanned aerial vehicles (UAVs) have gained significant popularity in recent years due to their ability to capture high-resolution aerial imagery for various applications, including traffic monitoring, urban planning, and disaster management. Accurate road and vehicle segmentation from UAV imagery plays a crucial role in these applications. In this paper, we propose a novel approach combining dual attention mechanisms and efficient multi-layer feature aggregation to enhance the performance of road and vehicle segmentation from UAV imagery. Our approach integrates a spatial attention mechanism and a channel-wise attention mechanism to enable the model to selectively focus on relevant features for segmentation tasks. In conjunction with these attention mechanisms, we introduce an efficient multi-layer feature aggregation method that synthesizes and integrates multi-scale features at different levels of the network, resulting in a more robust and informative feature representation. Our proposed method is evaluated on the UAVid semantic segmentation dataset, showcasing its exceptional performance in comparison to renowned approaches such as U-Net, DeepLabv3+, and SegNet. The experimental results affirm that our approach surpasses these state-of-the-art methods in terms of segmentation accuracy.

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