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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
Ransomware and artificial intelligence: a comprehensive systematic review of reviews Daengsi, Therdpong; Pornpongtechavanich, Phisit; Boonpoor, Paradorn; Wattanachukul, Kathawut; Puangnak, Korn; Phanrattanachai, Kritphon; Wuttidittachotti, Pongpisit; Horkaew, Paramate
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.v15i2.11107

Abstract

This study provides a comprehensive synthesis of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) in ransomware defense. Using a “review of reviews” methodology based on the PRISMA, this paper gathers insights on how AI is transforming ransomware detection, prevention, and mitigation strategies in the past five years (2020-2024). The findings highlight the effectiveness of hybrid models, which combine multiple analysis techniques such as code inspection (static analysis) and behavior monitoring during execution (dynamic analysis). The study also explores anomaly detection and early warning mechanisms before encryption that tackle ransomware’s growing complexity. It also examines key challenges in ransomware defense, such as techniques designed to deceive AI driven detection, and the lack of strong and diverse datasets. It highlights AI’s role in early detection and real-time response systems, enhancing scalability and resilience. With the systematic review of reviews approach, the contributions of this study are systematically consolidating research insights from multiple review articles, identifying effective AI models, and bridging theory with practice to foster collaboration among academia, industry, and policymakers. Future research directions are anticipated and practical recommendations for cybersecurity practitioners are provided. Finally, it presents a roadmap for advancing AI-driven countermeasures, for the protection of key systems and infrastructures against evolving ransomware threats.
A review of the hardware implementation of CRYSTALS-Kyber post-quantum cryptography algorithm Wijayanto, Ardhi; Ahmad, Nabihah; Ahmed, Salman
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.v15i2.10477

Abstract

The development of quantum computing is escalating the vulnerability of conventional cryptography algorithms. To answer this challenge, researchers develop the post-quantum cryptography (PQC) algorithms. The PQC algorithms are immune from attacks deployed by quantum computers. CRYSTALS-Kyber (abbreviated as Kyber) is a PQC algorithm, originally constructed as public key encryption (PKE), then extended as the key encapsulation mechanism (KEM) algorithm to securely transfer a shared key to other parties over unsecured communication channels. The implementation of Kyber algorithm in hardware ensures a better security standard for securing systems that prioritize performance. This study provides a literature review of the Kyber hardware implementations. The review is delivered by a systematic literature review method to discuss resource and performance optimization, key design constraints, performance trade-offs, and future research directions in hardware implementation of Kyber based on existing studies. Area utilization and energy efficiency are achieved through the optimization of memory and architecture. The trade-off between performance, flexibility, and utilization remains relevant in the deployment context. Future work should accommodate holistic solutions, security, and performance enhancements as well as fabrication for real-world solutions.
Leakage current mitigation and efficiency enhancement in transformerless 3-level NPC PV inverters Dhanapalan, Revathi; Balan, Sivasankari; Mangalaraj, Jayapriya; Thurai, Edwin Singh Chinna
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.v15i2.10763

Abstract

In this research explores the design and performance of a transformerless (TL) three-level neutral point clamped (NPC) inverter for grid-connected photovoltaic (PV) systems. In the push toward more compact, cost-effective, and efficient renewable energy systems, TL inverter topologies have emerged as a strong alternative to conventional systems that use line-frequency transformers. The proposed topology includes two separate PV sources interfaced through dedicated boost converters, which then feed into a common NPC inverter, enhancing flexibility and maximum power point tracking (MPPT) performance. Comprehensive simulations were performed using MATLAB/Simulink to evaluate grid compatibility, total harmonic distortion (THD), and transient behavior under fluctuating irradiance levels. Results validate the efficacy of the system in maintaining grid compliance, lowering leakage current, and achieving high-quality power output. Compared to traditional two-level inverters or transformer-based alternatives, the proposed system demonstrates a clear performance advantage in both efficiency and reliability.
Model predictive control with safety barrier enforcement for dynamic obstacle avoidance of mobile robots Gunarso, Michel Andrianto; Tamba, Tua Agustinus; Chandra, Jonathan
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.v15i2.11309

Abstract

This research proposes a model predictive control (MPC) approach with additional barrier function constraint for safe navigation of a differential drive wheeled mobile robot (DDWMR) in the presence of static and dynamic obstacles. The proposed approach uses the kinematic model of DDWMR to initially constructs a stabilizing MPC on the basis of Lyapunov’s stability theory. To ensure safe navigation of the DDWMR when obstacles are present, the control barrier function (CBF) concept is subsequently constructed and integrated into the developed MPC framework. The integrated MPC-CBF approach is shown to guarantee both the stability and safety properties of the DDWMR while navigating towards a desired goal position through a workspace filled with obstacles. The good performance of the proposed framework is demonstrated through computer simulations and experimental validation on a Turtlebot3 DDWMR plat form. In the real robot experiments, the controller achieved final tracking errors of [ex ey] = [0.1286 0.0626] m and e0 = 0.021 rad, while the corresponding simulation errors were [ex ey] = [0.0824 0.0698] m and e0 = 0.0883 rad, respectively. These results demonstrate that the developed feedback control method ensures safe, stable, and collision-free motions of the DDWMR.
FruitNet: a deep hybrid model for fruit detection and yield estimation Bijwe, Komal Baburao; Gadicha, Ajay B.
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.v15i2.11112

Abstract

In recent years, intelligent agriculture monitoring systems have attracted considerable interest for yield estimation and fruit quality inspection. This paper introduces FruitNet, a deep hybrid model integrating object detection, classification, and regression for automated apple detection and yield prediction. The architecture employs YOLOv8 for real-time detection, a convolutional neural network (CNN) for quality assessment across four categories (excellent, good, average, and bad), and random forest regression for estimating yield based on extracted features. To enhance classification robustness, the CNN features are further refined using a support vector machine (SVM) classifier, tuned via grid search for optimal performance. The system is implemented using Python and Django, with a preprocessing pipeline incorporating noise removal, data augmentation, and normalization. The model is trained and evaluated on the MinneApple dataset containing over 18,000 annotated images. Experimental results demonstrate high generalization performance with over 98% training accuracy and 94–95% validation accuracy across 15 epochs. Visual analytics including confusion matrices and detection overlays confirm robust detection and classification. The proposed FruitNet framework shows strong potential for deployment in real-time precision agriculture, supporting mobile integration, orchard-level insights, and scalable smart farming applications.
A firewall model for attack detection using machine learning and metaheuristic feature selection algorithms Abualhaj, Mosleh M.; Al-Khatib, Sumaya Nabil; Al-Shafi, Nida; Hiari, Mohammad O.; Sh. Daoud, Mohammad; Anbar, Mohammed; Al-Zyoud, Mahran M.
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.v15i2.9887

Abstract

This research presents a firewall model designed to enhance network attack detection by integrating machine learning (ML) and advanced feature selection techniques. The study introduces a union-based (DAUBA) feature selection method that combines the exploratory capability of the Dragonfly Algorithm (DA) with the exploitation efficiency of the Bat Algorithm (BA). By combining these two bio-inspired optimizers, the method generates complementary feature subsets that enhance both accuracy and efficiency. The proposed DA?BA feature selection method is incorporated into a ML–based firewall and evaluated on the UNSW-NB15 dataset using three classifiers: adaptive boosting (AdaBoost), K-nearest neighbor (KNN), and Naïve Bayes (NB). Experimental results demonstrate that the approach achieves near-perfect accuracy (100% with AdaBoost), along with strong precision, recall, and F1-scores, while maintaining computational costs compatible with real-time deployment. These findings highlight the novelty and practical value of combining DA and BA in feature selection for next-generation firewall systems.
Analytical broadband impedance matching using modified approximating functions with embedded transmission zeros Yerzhan, Assel; Manbetova, Zhanat; Mussapirova, Gulzada; Karnakova, Gayni; Mukhamejanova, Almira; Imankul, Manat; Kaliyev, Zhanybek; Bakirova, Nagima
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.v15i2.11220

Abstract

This paper proposes a modified approximating function (MAF)-based analytical method for broadband impedance matching in radio-electronic systems. Unlike traditional Chebyshev and Butterworth approaches, which rely on fixed pole distributions and predefined amplitude responses, the proposed method analytically embeds load-specific transmission zeros directly into the approximation function. This modification enables more accurate reconstruction of frequency-dependent impedance behavior without increasing the network order or circuit complexity. The method establishes a unified analytical synthesis framework linking impedance modeling, ladder-network realization, and constrained optimization. Validation was performed over the 1–10 GHz band using numerical simulations, Monte Carlo tolerance analysis, and prototype measurements. Compared with classical Chebyshev and Butterworth designs, the MAF-based approach achieves a 15–25% reduction in maximum reflection coefficient, a 30–40% decrease in optimization iterations, and improved robustness, with reflection variations remaining within 2% under ±10% parameter deviations. The results confirm that the proposed method provides superior analytical flexibility, improved matching accuracy, and reduced computational effort, making it suitable for automated broadband radio frequency (RF) design applications.
Deep neural networks for predicting kidney health: focus on cyst, stone, tumor, and normal classification Rabby, Abdey; Jannat, Jannatun Naima; Assaduzzaman, Md; Sarker, Rahmatul Kabir Rasel; Hasan Tusher, Raja Tariqul
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.v15i2.10084

Abstract

Kidney diseases affect individuals across all age groups and are a major global health concern. Pathological and other conditions, such as tumors, cysts, and stones, along with normal states of the kidneys, need to be detected as early as possible to improve treatment outcomes and quality patient care. This study looks into the use of computed tomography (CT) images for deep learning-based kidney disease classification. We evaluated four widely used convolutional neural networks (CNNs) such as VGG16, MobileNetV2, ResNet50, and InceptionV3 on a dataset of 12,456 CT images. Among the individual models, MobileNetV2 achieved the highest validation accuracy of 99.64%. As a novel contribution, we propose a hybrid deep learning model that combines MobileNetV2 and ResNet50 to enhance diagnostic performance. The hybrid architecture design led to superior results: 99.88% validation accuracy, 99.50% precision, 99.50% recall, 99.25% F1-score, and a reduced validation loss of 0.0090. Performance was further validated using confusion matrices, receiver operating characteristic (ROC) curves, classification reports, and 6-fold cross-validation to assess generalization. The proposed model demonstrates strong robustness and generalizability across kidney condition categories. As far as we are aware, not many research have looked into a hybrid combination of MobileNetV2 and ResNet50 for multi-class kidney CT classification.
Machine learning techniques for analyzing student’s performance in Islamic Studies Ramadani, Laili; Ardinal, Eva; Kamal, Muhiddinur; Ritonga, Mahyudin; Julhadi, Julhadi; Kardi, Juliwis; Nuraiman, Nuraiman
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.v15i2.8029

Abstract

Many learning institutions and organizations are currently faced with the acute burden of trying to forecast the academic performance of their students. This paper reflects the application of machine learning tools to discuss the potential and performance of students in Islamic Studies. The framework suggested in this paper, will start with the acquisition of the historical data of the students in the input dataset. First, the forward selection wrapper method is used to select the most meaningful features thus eliminating the redundant qualities in the set of student data. Three types of classifiers are then used to create a classification model based on fuzzy support vector machines (SVM), K-nearest neighbors (K-NN), and Naive Bayes. In such a methodological approach, academic performance is predicted and results measured according to certain criteria. According to results of the experiment, it is noted that feature selection-fuzzy support vector machine (FS-fuzzy SVM) has an excellent accuracy of 99.9% with a sensitivity of 98.50% and a specificity of 98.50% and it is therefore seen to be more effective in predicting the academic performance of students in Islamic Studies.
Smart aquaculture system: internet of things-based contextual e-service for sustainable aquafarming Al Rahib, Md Abdullah; Hussain, Md Redwan; Chowdhury, Md. Zubayer; Meraj, Jarin Tias; Sattar, Abdus
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.v15i2.10029

Abstract

A creative and contextual e-service for aquaculture system is developed to handle threats that climate change poses to global ecology and sustainable aquaculture. Therefore,"ICTization framework model," is based on capacity, connectivity, and context across three layers—core, and management, and distribution— has served as the study's guide. These layers support elements of centralization, decentralization, and parallelization hence reduce misconceptions about infrastructure, development, and communication. It incorporates a four-tier conceptual framework for an e-service system based on the internet of things (IoT) that is aimed at appropriately collect and manage environmental data in real time. Each tier of the framework is further elaborated for e-services using contextual information with existing infrastructure preserved while improving synchronization of data, administration, and services. It is able to predict and respond to variations in the quality, temperature, and other vital parameters of the water. The architecture realizes aquaculture as a service by orchestrating all the stakeholders – farmers, researchers, and network operators. E-service based system shall include remote monitoring, automated feeding and water quality assessment in real-time which will significantly improve efficiency reduce cost and increase income. Moreover, improved resource efficiency and less ecological impact made possible through the proposed conceptual framework enables sustainable aquaculture practices.

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