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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
An interpretable machine learning-based breast cancer classification using XGBoost, SHAP, and LIME Dutta, Monoronjon; Mehedi Hasan, Khondokar Md.; Akter, Alifa; Rahman, Md. Hasibur; Assaduzzaman, Md.
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.v13i6.7866

Abstract

Globally, breast cancer is among the most prevalent and deadly tumors that affect women. Early and accurate identification of breast cancer is essential for effective treatment planning and improving patient outcomes. This research focuses on improving breast cancer classification accuracy through machine learning (ML) methodologies, emphasizing interpretability. The study utilized the chi-square method to enhance model testing performance by pinpointing the most significant features for further analysis. The study also improved data quality by identifying and removing outliers, thus minimizing the influence of data irregularities on the performance of the models. For classification, the study evaluated six different ML algorithms—namely extreme gradient boosting (XGBoost), decision tree (DT), AdaBoost (AB), support vector machine (SVM), gradient boosting (GB), and K-nearest neighbors (KNN)—each applied to distinguish between the two variants of breast cancer. Among these, the XGBoost classifier emerged as the most accurate, achieving an impressive 99.30% accuracy rate. Moreover, the research incorporated shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) methods to boost the interpretability of the proposed model, offering crucial insights into the model’s decision-making process. Applying these interpretability techniques provided significant insights into the predictive factors influencing healthcare outcomes, ensuring the classification approach’s transparency and reliability.
Implementing advance control strategies to improve the performance of a microgrid Rajput, Isha; Verma, Jyoti; Ahuja, Hemant
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.v13i6.7746

Abstract

Integration of flexible and non-dispatchable renewable energy production will influence the operation and future expansion of prevailing power systems. Because of the variations in performance responses between microgrid (MG) and regular generators, including renewable energy sources-based (RES-based) MG into the electrical system may have an influence on stability analysis. The reduction in switching frequency induced by these energy processors electronic interconnected electricity producing sources has a detrimental impact on the system’s structural analysis, potentially leading to stability issues. Power infusion from RES-based MG, on the other hand, increases damping efficiency, reducing transmission line congestion and power shortage. As a result, in light of expanded MG information, it is important to analyse more complex stability problems and regulate the production of a power grid. This study will examine the effect of RES-based MG on the structural analysis and controller of a multimachine multi-area device in various scenarios This paper defines the growth of a one-of-a-kind proportional-integral-derivative (PID-based) power system stabilizer (PSS) type2 fuzzy partial order based on a meta-heuristic hybrid technique for refining the efficiency and robustness of harmonic currents.
Computationally efficient ResNet based Telugu handwritten text detection Revathi, Buddaraju; Prasad, M. V. D.; Gattim, Naveen Kishore
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.v13i6.8170

Abstract

Optical character recognition (OCR) is a technological process that converts diverse document formats into editable and searchable data. Recognition of Telugu characters through OCR poses a challenge because of compound characters. Identifying handwritten Telugu text proves difficult due to the substantial number of characters, their similarities, and overlapping forms. To handle overlapping characters, we implemented a segmentation algorithm that efficiently separates these characters, consequently enhancing the model’s accuracy. Feature extraction is a crucial phase in recognizing a broader range of characters, especially those that are similar in appearance. So, we have employed a light weighted ResNet 34 model that effectively addresses these challenges and handles deep networks without declining accuracy as the network’s depth increases. We have achieved a word level recognition rate of 81.5%. In addition, the parameters required by the model are less when compared to its counterpart inception V1, making it computationally efficient.
Dissipative soliton generation with sidebands using Bismuth Telluride (Bi2Te3) in erbium doped fiber laser Haris, Hazlihan; Awalin, Lilik Jamilatul; Muhammad, Ahmad Razif; Mustaffa, Siti Nasuha; Markom, Arni Munira; Hasnan, Megat Muhammad Ikhsan Megat; Harun, Sulaiman Wadi; Tan, Sin Jin; Saad, Ismail
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.v13i6.8175

Abstract

In this work, the demonstration of dissipative soliton (DS) was observed in erbium doped fiber laser (EDFL) using of Bismuth Telluride (Bi2Te3) nanosheets saturable absorber (SA). The prepared SA was deposited on a fiber ferrule using optical deposition method. Interestingly, the DS generatered was accompanied with sidebands and the number of sidebands grew with laser diode pump power. Sidebands were observed as a result of modulation instability (MI) process, which arises from the interaction between DS and nonlinear gain in the fiber laser cavity. Signal to noise ratio (SNR) of 58 dB was attained, confirming the stability of the generated pulse. This work proved the capability of Bi2Te3 as SA for generating DS with sidebands in an EDFL.
An optimation of advanced encryption standard key expansion using genetic algorithm and least significant bit integration Marjuni, Aris; Rijati, Nova; Susanto, Ajib; Sinaga, Daurat; Purwanto, Purwanto; Hasibuan, Zainal Arifin; Yaacob, Noorayisahbe Mohd.
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.v13i6.8367

Abstract

Ensuring data security in today’s digital landscape is of paramount importance, driving the exploration of advanced techniques for safeguarding confidential information. This study introduces a robust approach that combines advanced encryption standard (AES) encryption with key expansion, genetic algorithms (GA), and least significant bit (LSB) embedding to achieve secure data concealment within digital images. Motivated by the pressing need for enhanced data protection, our work addresses the critical challenge of securing sensitive information from unauthorized access. Specifically, we present a systematic methodology that integrates AES encryption for robust data security, GA for optimization, and LSB embedding for subtle information concealment. Through comprehensive experimentation, involving images such as ‘Lena.jpg,’ ‘Peppers.jpg,’ and ‘Baboon.jpg,’ we demonstrate the efficacy of our approach. The imperceptible modification rates mean squared error (MSE) of 0.199, 0.101, and 0.105, coupled with high peak signal-to-noise ratios (PSNR) of 10.04 dB, 9.95 dB, and 9.79 dB respectively, underscore the fidelity and subtlety of the embedded information. This study contributes to the ongoing discourse on data security by offering a comprehensive and innovative approach that addresses the evolving challenges in safeguarding digital information.
An attention-based channel estimation algorithm for next-generation point to point communication systems Olaniyi, Kayode A.; Heymann, Reolyn; Swart, Theo G.
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.v13i6.7064

Abstract

Accurate and robust estimation of channel parameters is essential in establishing reliable communication with characteristic optimal resource utilization in next-generation communication systems. Traditional techniques have limitations, such as the need for additional bandwidth and decreased spectral efficiency. Thus, there is a need for novel techniques that enhance the accuracy and robustness of channel parameter estimation in next-generation communication systems. To address this need, we propose in this paper a recurrent neural network (RNN)-based attention mechanism, to improve channel estimation accuracy and robustness in next-generation communication systems. The attention mechanism selectively focuses on the most relevant features while ignoring noise and interference. The attention network weights are initialized and are constantly updated in the course of network training. The weight values determine the significance of the features before passing them to the channel estimator. This allows the algorithm to adapt to varying channel conditions and improve its accuracy in challenging environments. The proposed attention-based algorithm performance is compared with three baseline techniques: learned denoising-based approximate message passing (LDAMP), Wasserstein generative adversarial networks (WGAN), and maximum likelihood (ML). The result evaluations indicate that the attention-based algorithm performs better than the existing artificial intelligence-based channel coding algorithms, in terms of robustness and accuracy.
Potential and economic feasibility analysis of solar-biomass-based hybrid system for rural electrification Channi, Harpreet Kaur; Giri, Nimay Chandra; Sandhu, Ramandeep; I. Abu El-Sebah, Mohamed; Syam, Fathy Abdelaziz
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.v13i6.7760

Abstract

A significant portion of the population lives in rural regions where the grid cannot provide them with enough power. Rising power demand, fossil fuel prices, limited fossil fuels such as coal, and environmental issues are the key drivers driving the usage of renewable energy resources for generating electricity. As a result, an alternate option for electricity generation in such remote places is required. Using renewable resources as alternatives would undoubtedly aid in mitigating the effects of global warming. The hybrid energy system combines electric power production with renewable sources such as solar, biomass, wind, biogas, hydro, and diesel generators (DGs). In light of this, a feasibility study on hybrid renewable energy was carried out for a specified remote region. This research investigates the efficacy of a solar-biomass-based hybrid power generation for rural electrification. The effective and sustainable alternative is found in a standalone hybrid version based on solar biomass. Electricity produced from the hybrid model proposed is $0.603.555 per unit, which is almost free of emissions of greenhouse gas (GHG), equally economical, and cleaner than the traditional supply. This system can be beneficial to electrify other adjacent remote zones.
Covid-19 forecasting model based on machine learning approaches: a review Sayeed, Md Shohel; Hishamuddin, Siti Najihah; Song, Ong Thian
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.v13i6.7304

Abstract

As coronavirus disease (Covid-19) it is a contagious disease that is spread by the SARS-CoV-2 virus, one of the most common causes of disease in humans. The disease was initially discovered in Wuhan, China, in 2019, and has now spread throughout the world, including Malaysia. A large number of people have lost their life partners and families because of this disease. Thus, in order for us to stop this epidemic spread, we have to implement social distance. The Covid-19 infection displays this type of behavior, which necessitates the development of mathematical and predictive modeling techniques capable of predicting possible disease patterns or trends, in order to assist the government and health authorities in predicting and preparing for potential outbreaks. The purpose of this paper is to provide an in-depth critique and analysis of the machine-learning approaches that have been implemented by researchers to predict Covid-19, based on existing research. As a result, future researchers will be able to use this paper as a valuable resource for their research related to the Covid-19 forecasting model.
Random sample consensus-based room mapping using light detection and ranging Latukolan, Merlyn Inova Christie; Pramudita, Aloysius Adya; Armi, Nasrullah; Hamdani, Nizar Alam; Susilawati, Helfy; Satyawan, Arief Suryadi
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.v13i6.6932

Abstract

Light detection and ranging (LiDAR) is a high-accuracy data source for geospatial providers that is displayed in two dimensions (2D) or three dimensions (3D). It is used to measure the distances or 2D or 3D maps of the environment. This study examines a random sample consensus (RANSAC)-based room mapping approach utilizing LiDAR. The RANSAC is used to achieve line fitting as a solution to acquire missing or incomplete point cloud data during the process of room scanning. The maximum x-y distance is proposed to achieve a proper model to fix the missing line during the LiDAR scanning process. Data retrieval uses ground-based LiDAR located in the middle of a certain room with the dimension of 5.76×4.95 m2. To explore a room mapping, a 2D LiDAR YDLIDAR G4 with an operating frequency of 7 Hz is used. The derived raw data is then visualized with MATLAB. The results show that the RANSAC can perform line-fitting for missing or illegible LiDAR point cloud data during the scanning process due to reflection or obstacles. The increase in the amount of data used is then directly proportional to the probability of the number of correct models.
Design and development of automatic voltage regulator using Ziegler-Nichols PID for electrical irons testing Sukma, Irawan; Suseno, Aji Dwi; Muhidin, Muhidin; Bakti, Prayoga; Ardiatna, Wuwus; Supono, Ihsan; Firdaus, Himma; Mandaris, Dwi
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.v13i6.7326

Abstract

This research presents an automated voltage regulation system crucial for a power input test of electric irons based on SNI IEC 60335-2-3 clause 11.4. The system is designed with an Arduino-based proportional-integral-derivative (PID) control mechanism to augment voltage stability and meet the standard requirement. The system comprises a microcontroller for PID control, a dimmer as the actuator, and a voltage sensor for error measurement. It utilizes the Ziegler-Nichols (Z-N) oscillation method to determine the PID control parameters. The simulation results identified a third-order transfer function as the best fit for the system, and the optimal PID parameters for the system are Kp=60, Ki=125, and Kd=500. The system was tested under the electric iron's active and non-active conditions. The proposed PID system demonstrated stable responses, effectively regulating the system voltage with minimal overshoot and settling time, and meeting standard requirements even under varying load conditions. It suggests potential applications beyond electric iron testing, promising efficiency improvements in broader household product testing.

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