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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.
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Articles 75 Documents
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Search results for , issue "Vol 15, No 2: April 2026" : 75 Documents clear
Optimized chaos based encryption-decryption of multimedia data Rasras, Rashad J.; Abu Sara, Mutaz Rasmi; Nader, Jihad; Alqadi, Ziad
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10939

Abstract

These days, a major concern is the security of multimedia data exchange over the internet. Compared to conventional methods, chaos-based encryption has proven increasing role and superiority in current multimedia cryptography. This research proposes an innovative multimedia encryption technique utilizing optimized chaotic systems and simple rearrangement operation. Data blocking will be used to optimize the chaotic model and to reduce the key generation time. To increase the private key length and to raise the level of data security the presented method will be implemented in multiple rounds, where each round will be treated as an independent task and the selected sequence of rounds to be executed depends on the user wish. The method can be efficiently used to cipher-decipher short messages, images, and digital speech signal, and it will use variable length of private key, variable block size and variable number of rounds. The output of simulation confirms effectiveness of proposed technique in giving strong security, and good speed up in ciphering and deciphering process comparing with other existing standard and chaotic based methods. The proposed technique shows high resistance to attacks while the quality of encrypted/decrypted digital data remains acceptable.
Dual-domain analysis of CGM data for personalized metabolic health management Gowriswari, Selvaraj; Brindha, Saminathan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10702

Abstract

Time-domain metrics on a continuous glucose monitoring (CGM) miss hidden dynamics, but frequency-domain features detect up on oscillating patterns, which makes it easier to measure glucose variability and gain clinical understanding. This study aimed to analyze the time and frequency domain features of CGM data for three patients (PtID 65, PtID 109, and PtID 134) to assess glucose variability and control. CGM data were collected over a specified period, with key metrics such as mean glucose, standard deviation (SD), time in range (TIR), and spectral features being computed. The analysis included both time domain and frequency domain features. PtID 109 exhibited the highest mean glucose (207.86 mg/dL) and greatest variability, reflected in the highest SD (85.57 mg/dL) and the lowest TIR (38.24%). PtID 134 showed the most stable glucose levels with the highest TIR (73.60%) and the lowest SD (59.81 mg/dL). Frequency domain analysis revealed that PtID:134 had the highest spectral flatness, indicating more variability in the glucose signal’s frequency content. PtID 109 had the lowest TIR at 38.24%, whereas PtID 134 achieved 73.60%, indicating a 24% enhancement and demonstrating superior glucose stability. Dual-domain analysis offers thorough framework for comprehending glucose variability, facilitating individualized treatment and enhanced metabolic health care.
Power sharing based on starfish optimization algorithm in DC microgrid Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Umar, Abubakar; Sabo, Aliyu; A. Shehadeh, Hisham
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9784

Abstract

This paper presents a starfish optimization algorithm (SFOA) method for optimizing control parameters in DC microgrids. SFOA is a new metaheuristic inspired by biology to solve optimization problems, which simulates the behavior of starfish, including exploration, preying, and regeneration. SFOA consists of two main phases of exploration and exploitation. This paper evaluates the performance of SFAO on droop control of DC microgrids by comparing with walrus optimizer (WO) and grasshopper optimization algorithm (GOA). From the simulation, SFOA shows superior capability. Validation on DC microgrid control using integral of time-weighted absolute error (ITAE) and integral of time-weighted squared error (ITSE). Simulation results demonstrate that the proposed technique exhibits a superior ITAE relative to WO and GOA, which are 6.88% and 8%, respectively. The performance validation results demonstrate that the SFOA approach exhibits potential and effective performance. The proposed method on DC microgrid control has been successfully applied and shows promising performance. The proposed methodology is particularly suitable for renewable energy integration in isolated or resource-constrained regions.
Exponential smoothing-based forecasting of self-similar internet of things traffic Mukhamejanova, Almira; Chezhimbayeva, Katipa; Kaliyeva, Samal; Lechshinskaya, Eleonora; Tumanbayeva, Kumyssay; Garmashova, Yuliya; Abisheva, Tolganay; Zhumay, Inkar
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11219

Abstract

The rapid growth of internet of things (IoT) devices generate highly variable and self-similar traffic patterns, creating challenges for maintaining quality of service (QoS) in modern telecommunication networks. Accurate short-term forecasting of such traffic is essential for efficient resource allocation, yet its fractal characteristics and long-range dependence complicate prediction. This study investigates the use of simple exponential smoothing for short-term forecasting of self-similar IoT traffic by evaluating three smoothing coefficients (a=0.1, 0.5, and 0.9). The Hurst exponent (H=0.5) confirms the presence of self-similarity in the observed traffic. Experimental results show that a=0.1 provides the highest prediction accuracy, achieving a mean absolute percentage error (MAPE) of 25.82% when forecasting traffic values within a 32-minute horizon. The method effectively captures underlying trends while reducing noise sensitivity. These findings demonstrate that exponential smoothing offers a lightweight, interpretable, and practical solution for real-time IoT traffic forecasting, supporting dynamic network load management under highly variable traffic conditions.
Challenges in applying DeepInsight for cyber threat detection AL-Essa, Malik; Qatawneh, Mohammad; Turab, Nidal; Alsarhan, Yazeed
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9649

Abstract

As the world suffers from intrusions and malware extensively nowadays, intru-sion detection systems (IDS) play a critical role in protecting cyberspace from attacks. However, attacks become more complex every day, leading to the neces-sity of developing new techniques that can protect our digital infrastructure from cyber-attacks. Deep learning (DL) is one of the techniques that are investigated to fight against cyber-attacks. However, due to the nature of traffic data, most of the techniques focus on the deep neural network (DNN) as the performance of the DNN dependsonthetraining data. In this paper, we investigate the effective-ness of using convolutional neural networks (CNN) to detect malware apps and network intrusions. The cybersecurity datasets are converted from tabular data into images using the DeepInsight technique. Experiments are conducted using two datasets, NSL-KDD and CICMaldroid20 datasets. The proposed method demonstrates that converting cybersecurity datasets from tabular data into im-ages may decrease the model’s accuracy. Furthermore, this approach introduces additional challenges in the detection of network intrusions and malware. More-over, the added architectural complexity may cause a dilution or distortion of feature representations, making it harder for the model to preserve the original semantic meaning of critical features.
Detecting respiratory diseases using spectrogram-based deep features and machine learning algorithms Hana, Elvina Nur; Faisal, Mohammad Reza; Kartini, Dwi; Mazdadi, Muhammad Itqan; Saputro, Setyo Wahyu; Indriani, Fatma; Satou, Kenji
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10585

Abstract

Early diagnosis of respiratory diseases is difficult as lung sound analysis requires the skills of medical professionals. Respiratory diseases are one of the leading causes of death in the world, so early detection is critical. Automatic identification is made possible by artificial intelligence. However, lung sound data is unstructured, while artificial intelligence often requires structured data. Therefore, feature extraction is required to structure the voice data. Traditional techniques such as mel-frequency cepstral coefficients (MFCC) often produce fewer features and information. This research uses a deep feature approach, which produces more features, as a solution. This research applies three convolutional neural network (CNN) architectures as deep features, namely VGG-16, DenseNet-121, and ResNet50, with machine learning classifications, namely random forest, support vector machine (SVM), Naïve Bayes, and K-nearest neighbors (KNN). This research will identify the optimal combination of methods. The results of this study show that respiratory disease classification can be effectively achieved by combining deep features and machine learning classification. The results of 10-fold cross-validation show that the three CNN architectures perform best on SVM with a linear kernel. The accuracy of VGG-16 is 70.63%, ResNet-50 is 64.93%, and DenseNet-121 is 73.58%.
High-performance voltage control of ladder boost converters using robust backstepping sliding mode control Le, Thanh-Lam; Bon, Nguyen Nhan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11061

Abstract

This paper presents a control-focused solution for high-gain DC-DC conversion by integrating a capacitor-diode ladder boost converter (CDLBC) with a backstepping sliding mode control (BSMC) scheme. While the CDLBC topology provides modular voltage gain at moderate duty ratios, the core contribution lies in the development of a robust control strategy to address the converter’s nonlinear and multistage dynamics. The proposed BSMC method combines the recursive design of backstepping with the finite-time convergence and disturbance rejection properties of sliding mode control (SMC). A saturation function is employed to suppress chattering and ensure smooth control action. The controller is derived based on Lyapunov stability theory and implemented on a digital signal processor without requiring hardware modification. Experimental results under various output voltage transitions show that the BSMC approach significantly improves transient performance, reduces voltage ripple, and enhances regulation accuracy compared to conventional SMC. These findings confirm the effectiveness and practicality of software-based robust control for efficient and adaptive DC-DC power conversion in renewable energy and embedded applications.
Hybrid analytical framework for evaluating socio-economic factors in regional development Akynbekova, Ayman; Muratkhan, Raikhan; Lamasheva, Zhanar; Mukhanova, Ayagoz; Yussupova, Gulbakhar; Eslyamov, Serik; Santeyeva, Saya; Abdrakhmanova, Alfiya
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11380

Abstract

This study aims to develop and validate a hybrid analytical framework for evaluating the influence of socio-economic factors on regional development. The framework combines correlation analysis, principal component analysis (PCA), and fuzzy inference modeling into a unified approach, applied to 2023 data from the city of Taraz, Kazakhstan, covering 16 socio-economic indicators across demographic, economic, social, and industrial domains. The findings reveal that investments in fixed assets (r=0.8963 and q=0.000010), average monthly salary (r=0.8907 and q=0.000010), and retail trade (r=0.8885 and q=0.000010) exert the strongest positive influence, while migration balance and manufacturing show weak or negative effects. The results demonstrate that the hybrid model offers more comprehensive insights compared to single-method approaches, validating its effectiveness in capturing complex and uncertain dependencies. Practically, the model provides policymakers with a robust decision-support tool for identifying priority areas, designing targeted strategies, and ensuring sustainable regional growth, with adaptability to other regions and datasets.
Taylor series linearization for fully fuzzy multi-objective fractional programming in educational systems Ganesan, Anitha; Kandasamy, Ganesan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10943

Abstract

This study examines the fully fuzzy multi-objective linear fractional programming problem (FFMOLFPP), whereby both the objective functions and restrictions incorporate fuzzy parameters represented as triangular fuzzy numbers (TFN), without converting them into crisp values. A hybrid solution approach is presented to tackle the intrinsic nonlinearity and uncertainty. Initially, the imprecise numbers are transformed into parametric representations via the y- cut method. A first-order Taylor series expansion is subsequently utilized to linearize each fractional objective function around a fuzzy decision point. The linearized objectives are then consolidated by the weighted sum approach, transforming the multi-objective fuzzy model into a single-objective linear program. Numerical examples validate the strategy and demonstrate the improved accuracy and efficiency of the proposed methodology.
Comparative analysis of ensemble learning algorithms in enhanced confidence-based assessments Azharludin, Nur Maisarah Nor; Samah, Khyrina Airin Fariza Abu; Dzulkalnine, Mohamad Faiz; Fadzil, Ahmad Firdaus Ahmad; Jono, Mohd Nor Hajar Hasrol; Riza, Lala Septem
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9935

Abstract

This paper provides a comparative analysis of ensemble learning (EL)algorithms to enhance the confidence-based assessment (CBA) in evaluating student performance. Traditional CBA often suffers from misclassification caused by overconfidence and underconfidence, limiting its accuracy and fairness. To address these challenges, an enhanced CBA-EL model integrating bagging and boosting ensemble algorithms is proposed. Five bagging algorithms, which are random forest (RF), decision tree (DT)support vector machine (SVM), K-nearest neighbors (KNN), Naïve Bayes(NB), and four boosting algorithms, which are adaptive boosting(AdaBoost), eXtreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were evaluated using a dataset of 276 responses collected from Pre- and Post-Quiz CBA in a discrete structures course. Algorithm performance was evaluated using accuracy, correlation, weighted mean precision (WMP), and weighted mean recall (WMR). RF achieved 73.19% accuracy, 0.725 correlation, 0.751 WMP, and 0.766 WMR, while CatBoost outperformed all with 86.23% accuracy and the highest correlation, WMP, and WMR values, with 0.842, 0.843, and 0.862, respectively. The findings indicate that integrating EL into CBA improves prediction accuracy and supports bias-aware student evaluation. This research advances reliable assessment practices and informs the development of adaptive learning systems.

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