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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 14, No 1: February 2025" : 75 Documents clear
Metamaterial inspired miniaturized ultra-wideband monopole hexagonal antenna with triple band-filter functions Elhabchi, Mourad; Bour, Mohamed; Atouf, Issam; Zaarane, Abdelmoghit
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7568

Abstract

In this letter, a new technique to the design of an ultra-wideband (UWB) monopole hexagonal antenna with triple band-rejected functions and to restrict the interferences with the exist bands is proposed, the design has the form of a hexagonal patch and a ground plane having rectangular shaped etched in the back side of the substrate to achieve the UWB behavior. The triple-band filter feature is generated by inserting a metamaterial (MTM) as a split ring resonator slots (SRRs) and a complementary split ring resonators (CSRRs) strip, thus no extra size is needed. The triple band-elimination is for 3.3-3.9 GHz centered at 3.5 GHz for 5G band, 4.99-5.4 GHz centered at 5.2 GHz for wireless local area network (WLAN) band, and 6.2-6.8 GHz centered at 6.5 GHz for IEEE INSAT/Supra-extended C-band. The antenna dimension has a compact size of 20×25×1.6 mm3. Current distribution on the antenna is used to analyze the effect of MTMs on the antenna operations. The simple structure and small size of the antenna makes it suitable for most of the wireless communication systems.
Optimal deployment of solar PV power plants as fast frequency response source for a frequency secure low inertia power grid K. Wamukoya, Brian; K. Kaberere, Keren; M. Muriithi, Christopher
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8548

Abstract

Modern power systems are witnessing increased uptake of solar photovoltaic power plants (SPVPPs) replacing conventional synchronous generators (SGs). SPVPPs lack any rotating parts resulting in no natural rotational inertia contribution to the grid. Reduced inertia makes the power system more dynamic, making it susceptible to frequency instability caused by minor disturbances. This problem is majorly addressed by limiting the penetration of SPVPPs to ensure a minimum level of critical inertia is maintained or by providing additional virtual inertia from an energy storage system. However, the SPVPPs can be configured to operate below maximum power point tracking (MPPT) (deloaded mode) to provide a reserve capacity that can rapidly be deployed as fast frequency response (FFR) in case of a frequency event. This paper presents a strategy to optimize the FFR capacity of a deloaded SPVPP using particle swarm optimization (PSO) algorithm. DIgSILENT PowerFactory was used to model the deloaded SPVPP and run time domain simulations. PSO algorithm was implemented using a Python script in PowerFactory. The proposed strategy was applied on a modified IEEE 39 bus test system. The results show that optimal deloading of SPVPP can help to successfully arrest frequency decline, reduce power curtailment while adhering to the prescribed constraints.
Progression of polymeric nanostructured fibres for pharmaceutical applications Abu Owida, Hamza; I. Al-Nabulsi, Jamal; M. Turab, Nidal; Al-Ayyad, Muhammad; Alazaidah, Raed; Alshdaifat, Nawaf
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7315

Abstract

Electrospinning has emerged as a simple and cost-effective technique for producing polymer nanofibers, offering a versatile approach for creating nanostructured fibers from a wide range of polymer materials. The pharmaceutical field has particularly welcomed the advent of electrospun nanofibers, as they hold immense potential for revolutionizing drug delivery systems. The recent surge of interest in electrospun nanofibers can be attributed to their unique characteristics, including elasticity and biocompatibility, which make them highly suitable for various biomedical applications. By incorporating functional ingredients into blends of nanostructured fibers, the capabilities and reliability of drug delivery devices have been significantly enhanced. This review aims to provide a comprehensive summary of recent research endeavors focusing on the concept of nanofibrous mesh and its multifaceted applications. With an emphasis on the simplicity of fabrication and the virtually limitless combinations of materials achievable through this approach, nanofibrous meshes hold the promise of transforming specific treatment modalities. By streamlining the delivery of therapeutic agents and offering enhanced control over drug release kinetics, nanofibrous meshes may herald a new era in targeted and personalized medicine.
Chronic disease prediction chatbot using deep learning and machine learning algorithms Sia, Mandy; Ng, Kok-Why; Haw, Su-Cheng; Jayaram, Jayapradha
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8462

Abstract

Ever since the rise of human civilization, more and more diseases have been discovered with the rapid growth of medical knowledge. This sheer volume of information makes it hard for humans to memorize or even utilize it efficiently. Thus, machine learning emerged as a powerful tool for complex calculations by offering a solution to this challenge. This paper intends to use deep learning and machine learning algorithms to develop a predictive model that can recognize potential diseases based on symptoms. The model is then seamlessly integrated into a text-based disease prediction assistant chatbot that serves as a communication platform between the users and the system. The algorithms researched for the disease prediction models are k-nearest neighbours (KNN), support vector machines (SVM), random forest, and neural networks. After that, a chatbot application is created by integrating long short-term memory (LSTM), natural language toolkit (NLTK) libraries, and Telegram. As a result, the SVM models demonstrated excellent performance by achieving an accuracy of 92.24%, closely followed by random forest with 92.23%, KNN with 91.57%, and artificial neural network (ANN) with 91.52% accuracy. In short, this paper presents a potential solution for a more accurate disease prediction tool by implementing the best disease prediction model with the chatbot models together.
A stereo-vision system for real-time person detection in ADAS applications using a fine-tuned version of YOLOv5 Rachidi, Oumayma; Ed-Dahmani, Chafik; Bououlid Idrissi, Badr
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8417

Abstract

Pedestrian detection holds significant importance in advanced driver assistance systems (ADAS) applications, and presents a challenging task in this field. While the advent of deep learning has facilitated the introduction of various pedestrian detectors characterized by accuracy and low inference speed, there persists a need for further improvements. Notably, ADAS requires accurate detection of pedestrians in various environmental conditions that can adversely impact the model’s performance, such as poor lighting, and bad weather. Furthermore, an imperative requirement involves the incorporation of distance estimation in conjunction with pedestrian detection, with an extension of detection capabilities to encompass cyclists and riders, who are equally crucial for ensuring road safety. Therefore, this paper introduces a stereovision system designed for the detection of pedestrians, cyclists, and riders. The initial phase, involves improving the performance of you only look once version 5 (YOLOv5s) through a fine-tuning process with a custom dataset integrating augmentation techniques to common objects in context (COCO) dataset. The detector is trained using Google Colab, and tested in real-time with a Raspberry Pi 4 model B, 8 G RAM. A comparative analysis is conducted between the YOLOv5s and the fine-tuned model to prove the accuracy of our approach. The results showcase a high performance of the detector reaching an accuracy exceeding 79%.
A novel recommender system for adapting single machine problems to distributed systems within MapReduce Orynbekova, Kamila; Kadyrov, Shirali; Bogdanchikov, Andrey; Oktamov, Saidakmal
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8370

Abstract

This research introduces a novel recommender system for adapting single-machine problems to distributed systems within the MapReduce (MR) framework, integrating knowledge and text-based approaches. Categorizing common problems by five MR categories, the study develops and tests a tutorial with promising results. Expanding the dataset, machine learning models recommend solutions for distributed systems. Results demonstrate the logistic regression model's effectiveness, with a hybrid approach showing adaptability. The study contributes to advancing the adaptation of single-machine problems to distributed systems MR, presenting a novel framework for tailored recommendations, thereby enhancing scalability and efficiency in data processing workflows. Additionally, it fosters innovation in distributed computing paradigms.
Swift and efficient cinnamon plant disease classification using robust feature extraction and machine learning techniques Thandapani, Sujithra; Selvaraj, Durai; Iqbal Mahaboob, Mohamed; Chakravarathi Bharathi, Venkatakrishnan; Vinayagam, Prabhu
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8254

Abstract

The extraction of features and textures holds a crucial significance in the realm of image processing and machine vision systems. Even though artificial intelligence (AI) techniques are superior in attaining the best results in image processing, several challenges remain open for further research in computation complexity, memory, and power requirements. In this context, robust preprocessing techniques are required to address such shortcomings and reduce the computational cost of predictive tasks. This paper employed two feature extraction levels to extract the best possible features from images of the cinnamon plant. Local directional positional pattern (LDPP) extracts global image features, while local triangular coded pattern (LTCP) extracts local features. It helps to provide detailed and more relevant information about the image texture. Once features are extracted, identifying and categorizing diverse textures within an image relies on recognizing their unique features. Typically, descriptors serve as the means for representing images in our work. Afterwards, we used ensemble learning to attain better classification results with the help of weak classifiers. Extracted features are provided to machine learning (ML) models like support vector machines (SVM), random forest (RF), and k-nearest neighbors (KNN) for better classification of the cinnamon category.
Predicting graduation in Moroccan open-access bachelors: early indicators and re-enrollment data Oqaidi, Khalid; Aouhassi, Sarah; Mansouri, Khalifa
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8580

Abstract

The primary aim of higher education institutions is the successful graduation of their students. This study explores open-access higher education in Morocco, introducing a predictive model for assessing the probability of students achieving a science bachelor's degree. We analyzed data from 2012 to 2022, initially encompassing 45,573 student entries, and narrowed it down to 14,054 records after data cleaning. Focusing on early academic indicators from enrollment onwards-excluding current program performance—we used popular machine learning classifiers to examine the predictive capacity for student graduation and early dropout. Our comparison included analyses with and without re-enrollment data. Upon analyzing various machine learning algorithms, we attained accuracies between 79% and 86%, identifying random forest (RF) as the superior model for predicting outcomes both with and without incorporating re-enrollment data. This analysis was grounded on initial indicators observed during enrollment and throughout subsequent years, deliberately excluding current academic performance metrics from consideration.
An opinionated sentiment analysis using a rule-based method Zeleke Mekonen, Mareye; Assegie, Tsehay Admassu; Palit, Shamik; Kalyan Kumar, Angati; Sinha Roy, Chandrima; Priya Kompala, Chandi; Kumar Napa, Komal
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8568

Abstract

The categorization of opinions into positive, negative, or neutral facilitates information gathering, pinpointing individual weaknesses, and streamlining the decision-making process. Precision in opinion classification enables decision-makers to extract valuable insights, make well-informed decisions, and execute suitable actions. Sentiment analysis is language-specific due to the distinct morphological structures unique to each language, distinguishing them from one another. This study implemented a rule-based sentiment analysis approach for Kafi-noonoo opinionated texts, leveraging a rule-based system tailored for smaller datasets that operate based on a predefined set of rules. The rule-based mechanism calculates the overall polarity of a given sentence by applying a set of rules and categorizes it into positive, negative, or neutral sentiments upon identifying sentimental terms from a dedicated file. While the analysis utilized 1,500 words sourced from Facebook and music review samples, the modest sample size yielded satisfactory results. Performance evaluation metrics such as precision, recall, and F-measure were employed, indicating positive word scores of 91%, 86%, and 88.4%, and negative word scores of 80%, 75%, and 77%, respectively.
Impact of usability on continuance usage intention in language learning apps with gamification features Ulfiah, Ulfiah; Rahthin Rasyadan, Muhammad Faris; Utami, Wening Tyas; Sunardi, Sunardi; Fitria Murad, Dina
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8023

Abstract

With the increasing popularity of language learning applications, gamification has become one of the approaches frequently integrated to enhance continuance usage intention (CUI). However, how the usability of these gamification features influences the intention to continue usage is not yet fully understood. Through the usability testing and system usability scale (SUS) method, this study evaluates the level of usability of gamification features in language learning applications with a novel research approach that also involves specific analysis regarding usability aspects according to Nielsen, including learnability, efficiency, memorability, errors, and satisfaction towards the CUI, categorized through the SUS statements grouping and then processed using the SPSS application. The study results indicate that the SUS scores show above-average scores for all three applications: Duolingo application at 77.08, Elsa Speak at 70, and Cake Learn at 70.58. Other findings suggest that usability aspects generally significantly influence CUI; however, only the satisfaction variable impacts CUI, which was observed only in Duolingo and Elsa Speak. These findings indicate that the overall usability of gamification features positively impacts CUI using language learning applications, thereby implying the need for continuous development.

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