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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
CaO:Tb3+ green-emitting phosphor for white light-emitted diode-phosphor applications: the improvement of light output intensity Tung, Ha Thanh; Nguyen Thi, Dieu An
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.v13i1.4753

Abstract

The use of CaO:Tb3+ with light green emission on for improvement of both luminescent output and chromatic fidelity of the white light emitted from a light-emitting diode (LED). The CaO:Tb3+ is combined with the yellow-emitting phosphor of YAG:Ce3+ to provide sufficient colored spectral proportion for the white light generation, enhancing the color performance. The phosphor combination is utilized for the three most applied LED structures: conformal, in-cup, and remote phosphor structures. The changes in optical properties of these three LEDs are monitored with adjustments in the proportion of CaO:Tb3+. The higher proportion of the green phosphor results in higher scattering efficiency in all structures, offering better color coordination and stronger luminous flux. The color quality scale is somehow reduced when CaO:Tb3+ concentration is more than a certain level. Therefore, depending on the phosphor configuration of the white light-emitting diode (WLED), the concentration of CaO:Tb3+ should be modified to achieve a good color rendition with improved color consistency and luminous properties.
Challenges in implementing free software in small and medium-sized enterprises in the city of Montería: a case study Baena-Navarro, Rubén; Vergara-Villadiego, Juan; Carriazo-Regino, Yulieth; Crawford-Vidal, Richard; Barreiro-Pinto, Francisco
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.v13i1.6710

Abstract

This study investigates challenges and opportunities in adopting free and open-source software (FOSS) in small and medium-sized enterprises (SMEs) in Monteria, Colombia. The research reveals that around 77.5% of SMEs prefer free software, yet surprisingly, 80% are unaware of the benefits of open-source licenses, with nearly 45% not adopting them due to lack of knowledge. Implementing FOSS in SMEs offers legal and economic advantages, including reduced software acquisition costs, compliance with data protection and privacy regulations, and fostering innovation. However, adoption barriers persist, necessitating further research for enhancing implementation in Colombian SMEs. Notably, Colombia's ethical framework for AI serves as a guide for ethical AI and open-source software deployment, aligned with sustainable development goals. This study highlights free software usage prevalence in Monteria's SMEs and critical factors hindering full adoption. Addressing challenges and leveraging potential benefits can improve efficiency, regulatory compliance, and contribute to sustainable development. Continued research in this field can promote broader and stronger implementation of FOSS in Colombian SMEs.
Efficiency and performance ratio of photovoltaics on a 50 kWp Universitas Pamulang Viktor rooftop solar power plant Rozak, Ojak Abdul; Triyanto, Aripin; Kusnadi, Heri
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.v13i1.5557

Abstract

To overcome the fossil energy crisis due to the increasing need for electrical energy, new renewable energy sources are needed. Due to technological developments in the fields of transportation, industry, household, and commercial use. Indonesia’s geography has the potential to apply new renewable energy, more specifically photovoltaic (PV). However, it is greatly influenced by environmental factors such as solar radiation, voltage, which have an impact on the output power efficiency and performance. So, it is necessary to test both measurements and calculations to see the optimization of output power and PV efficiency. From previous research, it has not been carried out, especially in the experimental method Universitas Pamulang: measurement and empirical and for a sufficiently high capacity with the aim of optimal output power. Methods of measuring sunlight intensity, voltage and current, the calculation of converting sunlight intensity to solar constellation, power, efficiency, and performance ratio (PR). The average value being 721 W/m2 an efficiency value of 19.9% and a value PR is obtained of 0.967 or 96.7% is still realistic. So that the system is declared optimal.
Developing a model for unmanned aerial vehicle with fixed-wing using 3D-map exploring rapidly random tree technique Dallal Bashi, Omar I.; K. Hameed, Husamuldeen; Al Kubaisi, Yasir Mahmood; H. Sabry, Ahmad
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.v13i1.5305

Abstract

While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.
Improving sentiment analysis using text network features within different machine learning algorithms Alnasrawi, Ali Mohamed; Alzubaidi, Asia Mahdi Naser; Al-Moadhen, Ahmed Abdulhadi
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.v13i1.5576

Abstract

Sentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB). Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.
Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques Hamida, Soufiane; Lamrani, Driss; El Gannour, Oussama; Saleh, Shawki; Cherradi, Bouchaib
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.v13i1.6313

Abstract

Significant health concerns are associated with skin diseases, and accurate and timely diagnosis is essential for effective treatment and patient management. To improve the classification of cutaneous diseases, we propose a novel hybrid system that incorporates the strengths of random forest (RF) and deep neural network (DNN) algorithms. The system employs data augmentation and balancing techniques to enhance model performance and generalizability. The HAM10000 dataset of diverse dermatoscopic images is used for training and evaluation in this study. In the hybrid system proposed, the RF model provides an initial diagnosis based on patient-reported symptoms, while the DNN analyzes images of skin lesions, resulting in more precise and efficient diagnoses. Using hyper-parameter optimization, we fine-tune the system for optimal performance. The evaluation demonstrates the accuracy of the hybrid model, which achieves a classification accuracy of 96.8% overall. According to our findings, the hybrid system demonstrates exceptional efficacy in six of seven skin disease classes. Variations in sensitivity and reliance on data quality and quantity are however cited as limitations. Nevertheless, this hybrid system has the potential to revolutionize skin disease diagnosis and treatment.
The effect of thickness of a conductive nanocomposite ink printed on textile co-planar waveguide antenna Mohd Radi, Nor Hadzfizah; Ismail, Mohd Muzafar; Zakaria, Zahriladha; Razak, Jeefferie Abd
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.v13i1.4775

Abstract

In the area of wearable technology an enhancement of basic microstrip antenna is evolution of wearable textile antenna. A new development of wearable antenna is the incorporated of conductive plane using nanocomposite ink that embedded onto the fabric. In this paper, the performance of variety thickness of conductive Graphene-Ag-Cu ink on a drill fabric is presented. The performances include its resistivity and conductivity measurement. By performing a measurement using scanning electron microscopy, energy-dispersive X-ray spectroscopy, and four-point probe, it can obtain and measure the composition and thickness of nanocomposite layered on a fabric and resistivity respectively. Hence, it can provide detailed information about the surface morphology, roughness, and thickness of the nanocomposite coating on the fabric as well as the electrical conductivity. Finally, the electrical conductivity increased to the fifth layered from 0.1473×104 S/cm up to 0.5393×104 S/cm.
Performance analysis of different methods for optimal sliding mode control of DC/DC buck converter Chlaihawi, Amer A.; Al-Modaffer, Ameen M.; Alhadrawi, Zaid
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.v13i1.5459

Abstract

Performance is always need to be considered in designing DC/DC buck converter. Despite the drastic use of DC/DC buck converter in industry, limitations due to unregulated voltage and current still persist. The dynamic performance of three methods of sliding mode control (SMC) were investigated. The comparative assessment of integral sliding mode control (ISMC) method, showed that the ISMC has an outstanding performance over the other tested methods of two variables with conventional SMC. The excellent performance of ISMC, under diverse operating conditions that include varying input voltage and load resistance, is achievable and it can provide a considerable edge over other control techniques in various field of industries, include electrical vehicle. The ISMC is highly preferable to overcome the problem of varying switching frequency, as well as optimizing power on transient response. The performance characteristic of ISMC shows fast dynamic response of various applications. Detailed simulations of the three SMC methods were carried out to validate the control algorithms using MATLAB/Simulink software.
Prediction of ionospheric total electron content data using NARX neural network model Shenvi, Nayana; Virani, Hassanali Gulamali
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.v13i1.6506

Abstract

Successful prediction of ionospheric total electron content (TEC) data will help in correction of positioning errors in global navigation satellite systems (GNSS) caused by the ionosphere. This research paper proposes a prediction model for ionospheric TEC using a nonlinear autoregressive with exogenous inputs (NARX) neural network that utilizes past TEC data alongwith solar and geomagnetic indices namely F10.7, disturbed storm (Dst), Kp, Ap, and time of the day. We assess the prediction capability of our model at different latitudes during different solar activity years. We compare our results with another NARX model which uses previous TEC data along with time of the day, day of the year and season as exogenous parameters. The results show that for the solar minimum year the TEC prediction accuracy improves by 35.71% and for the solar maximum year it improves by 31.20%. The results using root mean square error (RMSE), mean absolute error (MAE), correlation coefficient, and symmetric mean absolute percentage error (sMAPE) clearly indicate that solar and geomagnetic indices along with time of the day help in enhancing prediction accuracy of TEC across different latitudinal regions during both solar minimum and maximum years.
Improving frequency regulation for future low inertia power grids: a review Wamukoya, Brian K.; Muriithi, Christopher M.; Kaberere, Keren K.
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.v13i1.5873

Abstract

The modern power system is witnessing an unprecedented increase in the penetration of renewable variable generation (VG) sources. Increased uptake of converter interfaced VG like solar PV and wind power while replacing conventional synchronous generators (SGs) introduces new challenges to grid operators in terms of dynamically handling frequency stability and regulation. Reducing the number of SGs while increasing non-synchronous, inertia-less converter interfaced VG reduces grid natural inertia, which is critical for maintaining frequency stability. To cure inertia deficiency, researchers, broadly, have proposed implementing supplemental control strategies to VG sources or energy storage systems to emulate natural inertia (virtual inertia (VI)). Alternatively, VG sources can be operated below their maximum power point (deloaded mode), making available a reserve margin which can rapidly be deployed in case of a contingency with the help of power electronic devices, to provide fast frequency response. This paper reviews recent solutions proposed in literature to address the low inertia problem to improve frequency stability. Additionally, it highlights the formulation of an optimization problem for VI sizing and placement as well as techniques applied in solving the optimization problem. Finally, gaps in literature that require further research were identified

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