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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 6: December 2024" : 75 Documents clear
Comparing horizontal versus vertical arrangement on the ground resistance values Shamsul, Syakir Azim; Muhammad, Usman; Aman, Fazlul; Mohamad Nor, Normiza; Osman, Miszaina
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7944

Abstract

It is important to compare the horizontal electrodes versus vertical ground electrodes particularly when there is limited area to extend the horizontal ground electrode and hard soil at the deeper soil in order to install the vertical rod electrode. Although all of these can be assessed by computational work, much work has shown that computed resistance values are different than measured resistance values and these computational softwares are not always available to the users. For these reasons, the aim of this paper is to address this shortfall by considering two sites with two-layer soil resistivity model where site 1 with upper layer higher than the lower layer and vice versa for site 2. For the same size of ground electrodes, vertical arrangement is found to have lower ground resistance values, despite higher soil resistivity at the lower layer soil. Soil compaction after backfilling the trench during the installation of horizontal electrode has been identified as the main factor that contributes to differences between the measured and computed resistance values.
Advancing breast cancer prediction: machine learning, data balancing, and ant colony optimization Aouragh, Abd Allah; Bahaj, Mohamed; Toufik, Fouad
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8298

Abstract

Breast cancer constitutes a significant threat to women's health worldwide. The World Health Organization (WHO) reports around 2.3 million new cases each year, making this disease the primary reason for cancer-related fatalities among women. In light of this alarming situation, developing innovative tools for early detection and optimal treatment is imperative, as it directly addresses the pressing need to enhance our capabilities in the quest to overcome breast cancer. This study fits in with this approach, introducing a comparative assessment of multiple machine learning algorithms and integrating data preprocessing, data balancing and feature selection techniques. The studied Coimbra dataset, composed of 116 records and including 10 medical characteristics, exhibited promising performance in all classification metrics, reaching an accuracy of 89.74%, and an area under the receiver operating characteristic curve (AUC-ROC) of 89.68%. These findings highlight the significant potential of our approaches to improve breast cancer treatment and detection systems, providing health practitioners with more efficient resources.
Understanding explainable artificial intelligence techniques: a comparative analysis for practical application Bhatnagar, Shweta; Agrawal, Rashmi
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8378

Abstract

Explainable artificial intelligence (XAI) uses artificial intelligence (AI) tools and techniques to build interpretability in black-box algorithms. XAI methods are classified based on their purpose (pre-model, in-model, and post-model), scope (local or global), and usability (model-agnostic and model-specific). XAI methods and techniques were summarized in this paper with real-life examples of XAI applications. Local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) methods were applied to the moral dataset to compare the performance outcomes of these two methods. Through this study, it was found that XAI algorithms can be custom-built for enhanced model-specific explanations. There are several limitations to using only one method of XAI and a combination of techniques gives complete insight for all stakeholders.
Deep learning based detection, classification, and location of power system faults Sahoo, Anjan Kumar; Samal, Sudhansu Kumar
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7239

Abstract

The identification, categorization, and localization of faults play a crucial role in maintaining the smooth operation of power systems. Distance relays possess a significant capability to withstand power fluctuations, thereby minimizing inadvertent disruptions in transmission lines. Addressing these challenges involves the adoption of advanced fault analysis techniques to enhance the accuracy and speed of relay operations. While modern machine learning (ML) approaches are still nascent in fault analysis, the authors propose a novel deep learning (DL) based long short term memory (LSTM) method for precise fault detection, classification, and rapid fault location estimation. The proposed approach is applied to the Kundur two-area 4 machine 11 bus system covering a distance of 220 km. The LSTM fault detection (LSTM (FD)) module accurately detects and classifies faults, while the LSTM fault location (LSTM (FL)) module precisely estimates fault locations. The effectiveness of the proposed method is verified through a comparative assessment with various traditional ML and DL techniques. The protection modules are also tested under different fault locations, fault resistances, and noisy signals. The features taken into consideration for the operation of the protection modules are different bus voltages, bus currents, zero sequence voltage, zero sequence current, fault inception angle, and fault resistance.
Sustainability dimensions in enhancing the energy and resource efficiency of big data systems D/O Arunachalam, Aishwharya Raani; Jusoh, Yusmadi Yah; Abdullah, Rusli; Umarova, Zhanat; Akhmetova, Sabira; Iztayev, Zhalgasbek; Zhumatayev, Nurlybek
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8052

Abstract

Big data systems are essential for many businesses to grow, leveraging the vast amounts of data they generate and access. However, big data systems are plagued by significant sustainability challenges. Thus, this study aims to identify metrics that can measure the sustainability of big data systems. This research conducted a comprehensive literature review to identify five key sustainability dimensions: technical, environmental, economic, social, and individual. Then, a set of 29 metrics corresponding to these dimensions was developed. To ensure the relevance and applicability of these metrics, an expert validation session was carried out with five experts in the big data field. The validation process confirmed the appropriateness of our proposed metrics and modification take place. The findings of this study present 30 metrics upon experts’ validation that could enhance the sustainability of big data systems, offering meaningful insights for researchers and practitioners aiming to enhance resource and energy efficiency in this domain.
Solar photovoltaic integrated load frequency control of power system using variable structure fuzzy controller Masikana, Sboniso; Sharma, Gulshan; Sharma, Sachin
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7806

Abstract

The incorporation of renewable energy sources into modern power systems is on the upswing, intending to produce and deliver cost-efficient electricity to meet the ever-increasing demands of today’s world. Solar energy stands out as a plentiful and robust solution for meeting current electricity requirements. However, integrating solar photovoltaic (PV) generated power into the contemporary power system introduces complexity, necessitating the development of suitable control design to ensure effective regulation of load frequency control (LFC). This research paper concentrates on the mathematical modeling and integration of solar PV generated electricity into a hydrothermal system. In addition, this study also evaluates the performance of the variable structure fuzzy (VSF) control with reduced rule base for hydrothermal system concerning varying degrees of disturbances in one or in both regions of the power system. Moreover, the research reveals that the integration of PV power into hydro-thermal systems can improve the LFC outputs and mitigate system deviations in the face of different disturbance scenarios.
Reconfiguration of the radial distribution network using an artificial rabbits optimization approach Rao, Ganney Poorna Chandra; Krishna, Puvvula Venkata Rama; Rupesh, Mailugundla; Karike, Swathi; Polisetty, Sathyanarayana; Reddy, Sareddy Venkata Rami; Sreedhar, Jadapalli
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7260

Abstract

Lowering system power losses along with improving voltage profile have been major concerns for researchers for the past few decades. The performance of an electrical distribution system (EDS) is dependent on these two factors. This work’s main emphasis is on reconfiguring the radial distribution network (RDN) to diminish system power losses and strengthen the voltage profile. The process of network reconfiguration (NR) involves state transitions of sectionalizing and tie switches while still adhering to the limitations. In this work, the optimal reconfiguration network is determined using the artificial rabbits optimization (ARO) approach. The adopted method is tested using IEEE 119 bus RDN under low, normal, and heavy load conditions. When compared to the current approaches, the adopted methodology produced favorable results.
A novel agile method for user stories’ XMI model generation via NLP and MDA Kharmoum, Nassim; Retal, Sara; Hajjaj, Mouna; Lagmiri, Souad Najoua; Rhazali, Yassine
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7290

Abstract

Agile software development methodologies have grown in popularity during the past few years. One of the key components of agile development is the use of user stories to describe software requirements. However, creating and managing user stories can be time-consuming and error-prone. In this paper, we present a novel method to generating user stories’ XMI model using natural language processing (NLP) and model-driven architecture (MDA) approach. We devel-oped a method that uses NLP to extract key information from user stories and then applies MDA techniques to generate an XMI model conforming to its pro-posed meta-model. We conducted a case study to illustrate and validate our method, and we analyze and discuss the studied-related work with our proposal. As a result, our method has the potential to make user stories’ models and their meta-models the focus of software development. This will help to streamline the development process by making it easier to construct and transform models in an agile environment with the MDA approach.
Employing PIPRECIA-S weighting with MABAC: a strategy for identifying organizational leadership elections Setiawansyah, Setiawansyah; Hadad, Sitna Hajar; Aldino, Ahmad Ari; Palupiningsih, Pritasari; Fitri Laxmi, Gibtha; Megawaty, Dyah Ayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7713

Abstract

The election of organizational leaders, especially in organizations whose members have diverse backgrounds and interests, can cause various problems. Problems in the selection of school organization leaders include the absence of an objective selection of organizational leadership candidates because they are selected based on comparisons between candidates without considering the criteria in the selection of organizational leadership candidates. Research related to the multi-attributive border approximation area comparison (MABAC) and simplified pivot pairwise relative criteria importance assessment (PIPRECIA-S) methods has never been conducted so far, so it is a reference in conducting this research using the MABAC and PIPRECIA-S methods. This study aims to select the head of the school organization using the MABAC method and PIPRECIA-S weighting can increase the objectivity of the criteria assessment results by relying on calculations from the PIPRECIA-S weighting method. Based on the selection results using the MABAC method and PIPRECIA-S weighting, candidate 1 was recommended as the leader of the school organization because it achieved rank 1 with a total score of 0.293. The contribution of this research is to help in the selection of the head of the organization using the PIPRECIA-S and MABAC methods as a decision-making solution.
Exploring bank account information of nominees and scammers in Thailand Sirawongphatsara, Patsita; Pornpongtechavanich, Phisit; Sriamorntrakul, Pakkasit; Daengsi, Therdpong
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8042

Abstract

In today's digital era, people heavily depend on the internet for various tasks, such as online banking and e-commerce. While online transactions offer convenience, they also expose vulnerable individuals to potential exploitation by online scammers. The analysis and inquiry presented herein rely on data sourced from ChaladOhn, a system developed by academics and law enforcement, covering the period from February 2022 to January 2023. The comprehensive investigation reveals that each case resulted in losses under 10 million Thai Baht, accumulating to a staggering 3,100 million in damages. Notably, the fraudulent activities were traced back to the top two banks in the Thai market, referred to as the first and second bank. These banks were found responsible for; i) 28.2% and 16.0% of all scam accounts, ii) 25.6% and 20.5% of all transactions, and iii) 35.7% and 14.9% of all victim losses, respectively. The results of the inquiry must be shared with appropriate organizations and regulators due to the predicted worsening of this situation. This proactive approach aims to facilitate the development, recommendation, and implementation of effective strategies to address the escalating threat of online scams.

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