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30,609 Article Results

Enhanced detection of chronic obstructive pulmonary disease via exhaled breath analysis: internet of things and electronic nose system

10.11591/ijeecs.v42.i3.pp875-883
Nur Hidayah Naimah Harahap , Budi Yanti , Muhammad Ilham , Muhammad Suhaili , Dzakiroh Mufidah Hasibuan , Farah Narizki
Chronic obstructive pulmonary disease (COPD) remains a major global health burden, highlighting the need for accessible, non-invasive screening tools. This study aims to develop a portable, real-time internet of things (IoT)-integrated electronic nose (e-nose) system for COPD detection using exhaled volatile organic compounds (VOCs). Breath samples from 44 participants (healthy, smokers, and COPD) were analyzed using a MOS based e-nose, and four machine-learning classifiers were evaluated. Data were processed through cloud-based pipelines enabling real-time acquisition and automated analysis. The random forest (RF) model achieved the highest performance (accuracy 86%) in distinguishing COPD-related VOC patterns. This approach overcomes limitations of earlier offline Tedlar-bag methods by enabling direct, real-time breath analysis. The prototype dashboard provides immediate visualization for potential remote monitoring. Key limitations include the small sample size and non-standardized breath sampling, which may affect VOC variability. Overall, this work contributes a cost-effective, portable, IoT-enabled framework demonstrating the feasibility of real-time VOC analysis for early COPD screening and future integration into telehealth and community-based diagnostics.
Volume: 42
Issue: 3
Page: 875-883
Publish at: 2026-06-10

Improved interactivity and automated response for visual question answering

10.11591/ijeecs.v42.i3.pp742-752
Nguyen Ha Manh Khang , Nguyen Tuan Anh , Nguyen Minh Hoang , Bui Thanh Hung
Visual question answering (VQA) systems have made substantial progress, yet they still face limitations in handling complex or ambiguous queries and supporting real-time interaction due to reliance on large, computationally expensive models that increase latency and restrict practical deployment, particularly in educational contexts. This study aims to develop an efficient and interactive VQA system that enhances answer accuracy while enabling natural two-way communication with users. To achieve this goal, we propose a lightweight multimodal framework based on pre-trained vision language models such as BLIP and fine-tuning T5, combined with prompt engineering to improve question understanding and answer generation. The system further incorporates conversational context memory and a feedback mechanism that generates clarification questions when user inputs are ambiguous, thereby strengthening interaction capabilities. Experiments are conducted on public benchmark dataset Flickr8k, using single-GPU computational settings to evaluate accuracy, response latency, and interaction effectiveness. The experimental results demonstrate that the proposed approach achieves competitive or superior accuracy compared to heavier baseline models, while significantly reducing inference time and enabling real-time interaction. The main contributions of this work include a lightweight, prompt-driven VQA architecture, an interactive strategy for resolving ambiguous queries, and empirical evidence that efficient models can support accurate and conversational VQA for education and other real world applications.
Volume: 42
Issue: 3
Page: 742-752
Publish at: 2026-06-10

Parameter identification and continuous friction modelling of a brushed DC motor

10.11591/ijeecs.v42.i3.pp699-707
Ahmad’Abdan Syakuro , Vani Virdyawan , Sri Raharno , Indrawanto Indrawanto , Tegoeh Tjahjowidodo
Designing high-performance control systems for brushed DC motors is often hindered by the lack of comprehensive dynamic parameters in manufacturer datasheets, particularly for low-cost DC motors. In addition to the parameter identification method, this study also introduces a continuously differentiable friction model incorporating Coulomb and viscous-like behaviors using a hyperbolic tangent function. The electrical and mechanical parameters of an RS-775 motor were identified using standard laboratory tools and the MATLAB system identification toolbox. The proposed model was validated against experimental data under square wave and sinusoidal inputs, achieving a position prediction error of less than 5% and capturing complex dynamic behaviors. The results demonstrate that this accessible identification approach provides a sufficiently accurate dynamic model for educational and industrial robotics applications, offering a superior alternative to trial-and-error tuning.
Volume: 42
Issue: 3
Page: 699-707
Publish at: 2026-06-10

Smart contracts and a dual blockchain structure for collaborative tourism

10.11591/ijeecs.v42.i3.pp892-901
Zohra Temmar , Asmaa Boughrara
This article optimizes a decentralized system for collaborative tourism in Alge ria using blockchain, smart contracts, and proof of reputation (PoR) consensus. The system matches services into organized trips, manages reservations, and automates payments to ensure transparency and autonomy without centralized authority. This work opens the door to exploring dual-blockchain architectures. Building on a previous work, we enhanced node interactions, automated con tract execution, and introduced a dual-blockchain structure to reduce latency while improving scalability and security.
Volume: 42
Issue: 3
Page: 892-901
Publish at: 2026-06-10

Enhancing the performance of 3-phase induction motors by developing a 40-degree asymmetrical coil design for the stator coil

10.11591/ijeecs.v42.i3.pp678-687
Zuriman Anthony , Refdinal Nazir , Muhammad Imran Hamid
The use of three-phase induction motors in industry is very widespread due to their durability, simplicity, cost, and ease of use. These motors are continuously developed to improve performance. However, the increase in motor performance is directly proportional to the increase in the motor’s cost. Therefore, innovative solutions are needed to make three-phase induction motors more efficient without raising the motor’s price. The study’s goal is to create a motor coil design that won’t cost a lot more but will make 3-phase induction motors more efficient. This study developed a 2-layer coil design with a pair of poles for each layer. The second coil layer is located 40 electrical degrees away from the first layer. In the lab, the motor’s performance was assessed and compared to a conventional motor. This new motor uses identical materials as the conventional model; therefore, it does not incur additional costs. The results showed that this method could increase the rotor speed, output power, efficiency, and load torque of the motor by 0.21%, 16.29%, 15.90%, and 16.05%, respectively.
Volume: 42
Issue: 3
Page: 678-687
Publish at: 2026-06-10

A computational framework for detection, classification, and visualization of magnetic nulls in multi-spacecraft observations

10.11591/ijeecs.v42.i3.pp865-874
Sri Ekawati , Dongsheng Cai , Hiroyuki Kudo
Magnetic nulls, defined as locations where the magnetic field magnitude be comes zero, are theoretically well defined however practically difficult to lo cate, validate, and interpret. To address these challenges, this paper introduces a novel, modular, and fully reproducible automated framework for magnetic null detection, classification, and visualization based on multi-spacecraft observations. The proposed framework consists of two open-source modules: an automated data ingestion and null detection module, and a topological classification and three-dimensional visualization module. Magnetic nulls are detected by combining eigenvalue analysis of the magnetic field gradient tensor with tetrahedron-based geometric validation, enabling both numerical stability assessment and physical consistency checks. Meanwhile, detected nulls are classified into radial (Type A, B) and spiral (Type As, Bs) topologies, and their lo cal magnetic structures are visualized through reconstructed three-dimensional magnetic field lines. The main contribution of this work is the tight integration of detection, numerical validation, classification, and visualization within a single end-to-end pipeline, ensuring consistency between computational output and physical interpretation. The framework is validated using four previously reported electron diffusion region (EDR) events and one storm-time substorm event. The detected null times closely agree with the reported EDR intervals, with several events showing sub-second differences. Among all detected can didates, three nulls satisfy strict numerical validity criteria, including a Type A null, a Type As null, and a Type Bs null.
Volume: 42
Issue: 3
Page: 865-874
Publish at: 2026-06-10

Hybrid plugin for detecting illicit images on the internet using EfficientNet convolutional neural networks

10.11591/ijeecs.v42.i3.pp809-817
Christine Laure Mananga , Felix Paune , Léandre Nneme Nneme
The proliferation of illicit visual content on the internet, such as pornography and violent imagery, presents a growing societal concern. This paper proposes the design and implementation of a lightweight browser-integrated plugin that utilises a hybrid approach combining content based filtering with convolutional neural networks (CNNs), specifically the EfficientNetB7 architecture, to detect and block illicit images in real time. Developed using Python and TensorFlow, the plugin was trained on a curated dataset comprising NSFW, DeepNude, and safe-for-work (SFW) images. Experimental results on a dataset of 1,064 randomly selected images demonstrated a detection accuracy of 99%, with a processing time of 92 seconds and a 7% combined false positive and false negative rate. The plugin is compatible with Chrome browsers and contributes to safer online experiences, particularly for children, educators, and users in sensitive environments.
Volume: 42
Issue: 3
Page: 809-817
Publish at: 2026-06-10

Statistical comparison of MLP and LSTM for mobile health sentiment analysis

10.11591/ijeecs.v42.i3.pp818-826
Ghanim Kanugrahan , Win Ce , Vito Hafizh Cahaya Putra , Yudi Ramdhani , Febriyanti Panjaitan
This study investigates user sentiment towards the Mobile JKN public health application by applying text classification models based on deep learning. Two approaches were compared: a multi-layer perceptron (MLP) with TF IDF features and long short-term memory (LSTM) with Word2Vec embeddings. The dataset consists of 114,364 Indonesian-language user reviews collected from the Google Play Store. To address class imbalance, we applied random oversampling. Each model was evaluated using 5-fold stratified shuffle split cross-validation. The results showed that MLP models achieved higher accuracy (up to 83.90%), while LSTM models demonstrated better recall and precision on minority classes such as neutral sentiment. However, statistical validation using the Wilcoxon signed-rank test revealed that the performance differences between models were not statistically significant (p > 0.05). These findings suggest that both models are viable for sentiment analysis, with trade-offs depending on the evaluation metric of interest. Future work may explore hybrid architecture and larger datasets for improved performance and statistical confidence.
Volume: 42
Issue: 3
Page: 818-826
Publish at: 2026-06-10

A transfer learning approach for real-time detection and classification of Indonesian coins

10.11591/ijeecs.v42.i3.pp856-864
Nur Hadisukmana , R. B. Wahyu , Stewart Qiu
Automated currency recognition plays an important role in banking automation, retail systems, and assistive technologies. While banknote recognition has been extensively studied, coin recognition remains challenging due to small object size, metallic reflectance, visual similarity across denominations, and circulation-induced wear. This study proposes a real-time system for detecting and classifying Indonesian coins using a transfer learning–based deep learning approach. A curated dataset was developed to address the lack of publicly available training data for this domain. The model was initialized with pretrained weights and fine-tuned to adapt to the specific coin classification task. Experimental evaluation on an unseen test set demonstrates high detection accuracy while maintaining real time inference performance. Qualitative analysis under challenging conditions—including glare, low illumination, occlusion, and coin wear— reveals operational limitations and defines robustness boundaries. The findings confirm that frozen-backbone transfer learning provides an effective and computationally efficient strategy for adapting state-of-the-art object detectors to low-resource, domain-specific currency recognition tasks.
Volume: 42
Issue: 3
Page: 856-864
Publish at: 2026-06-10

Emulation-based evaluation of dust-aware automated cleaning system for aggregated solar panels on electric vehicles

10.11591/ijeecs.v42.i3.pp637-648
Mohamed Abubakr Mahgoub Hassan , Belal Ahmed Hamida , El-Sayed Soliman A. Said , Muhammed Zaharadeen Ahmed
The integration of photovoltaic (PV) panels into electric vehicles (EVs) provides a complementary energy source capable of extending driving range and reducing reliance on grid-based charging. However, the practical contribution of vehicle-mounted PV systems is significantly constrained by dust accumulation, which can induce power losses exceeding 20% under prolonged urban and roadside exposure. This study presents a low-power; sensor-driven, automated dust detection and cleaning system specifically designed for aggregated EV-mounted solar panels. Hybrid series–parallel panel aggregation architecture is employed to mitigate mismatch and partial shading effects associated with non-uniform dust deposition. A MATLAB/Simulink-based emulation framework is developed to model dust-induced attenuation, capacitive sensor response, cleaning subsystem energy consumption, and net energy recovery under static parking, urban driving, and mixed-use operating conditions. Results demonstrate that the proposed system maintains panel performance within 95%–98% of clean baseline output and recovers approximately 12%–15% of the dust-induced lost energy per cleaning cycle, while sustaining a positive net energy balance with minimal operational overhead. The main contributions of this work include the development of a quantitative energy trade-off model linking dust density, sensor response, and cleaning cost, the design of an EV specific hybrid aggregation strategy for dust-resilient power extraction, and a reproducible emulation framework for evaluating autonomous cleaning systems under realistic vehicular conditions. These findings confirm the technical feasibility and energy efficiency of intelligent dust mitigation as an enabling mechanism for solar-assisted electric mobility.
Volume: 42
Issue: 3
Page: 637-648
Publish at: 2026-06-10

Resilient artificial intelligence, secure digital ecosystems, and intelligent computing for a connected future

10.11591/ijeecs.v42.i3.pp631-636
Tole Sutikno
This editorial introduces the articles published in Volume 42, Number 3, June 2026 of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), highlighting recent advances and emerging research directions in artificial intelligence, cybersecurity, intelligent computing, and digital transformation. The published studies span a broad range of topics, including machine learning, intelligent analytics, healthcare technologies, computer vision, cryptography, privacy-preserving systems, Internet of Things (IoT) security, cloud computing, distributed optimization, blockchain applications, and decentralized digital platforms. Emerging trends highlighted throughout this issue include foundation and multimodal AI models, AI-enabled cybersecurity, privacy-preserving machine learning, Zero-Trust architectures, edge intelligence, decentralized computing, and digital trust ecosystems. Collectively, these contributions underscore the growing importance of resilient artificial intelligence and secure digital infrastructures in enabling adaptive, efficient, and trustworthy connected environments. The increasing integration of intelligence, security, resilience, and human-centered design principles reflects the evolving requirements of next-generation technologies that support sustainable innovation, economic development, and societal well-being. The research presented in this issue provides valuable perspectives on the opportunities and challenges associated with building secure, resilient, and intelligent digital ecosystems for an increasingly interconnected future.
Volume: 42
Issue: 3
Page: 631-636
Publish at: 2026-06-10

Multi-objective task scheduling in large-scale distributed systems using a Lévy flight-based hybrid Bat-Whale optimization algorithm

10.11591/ijeecs.v42.i3.pp913-926
Ali Mohammed Ahmed , Manar Younis Kashmola
The rapid growth of cloud computing demands efficient task scheduling strategies capable of handling heterogeneous resources, dynamic workloads, and multiple conflicting objectives. Existing approaches often optimize a single criterion, limiting their effectiveness in large-scale distributed systems. This paper proposes hybrid Bat–Whale optimization algorithm (BWOA), a hybrid scheduling algorithm combining the Bat algorithm and Whale optimization algorithm, enhanced with Lévy flight-based exploration, adaptive crossover, and a smart local search mechanism. The framework balances global exploration and local exploitation while preserving population diversity and intensifying search around promising solutions. A problem-aware local search reallocates long-duration tasks to high performance virtual machines and selectively swaps tasks with poor response times. Experiments on a heterogeneous cloud environment with 300 tasks and 50 virtual machines, using min–max scaling for workload normalization, demonstrate that BWOA outperforms classical methods, including first come, first served (FCFS) and Min-Min scheduling algorithms, achieving superior makespan (≈32.77 s) while maintaining competitive utilization, throughput, and energy efficiency. These results highlight the effectiveness of hybrid metaheuristic approaches integrating multiple optimization strategies for multi-objective task scheduling in large scale cloud systems, providing a robust and scalable solution for both academic research and practical deployment.
Volume: 42
Issue: 3
Page: 913-926
Publish at: 2026-06-10

Optimization of glioma segmentation using 3D U-Net++ in MRI surgical planning and patient safety outcomes

10.11591/ijeecs.v42.i3.pp798-808
Ahmed Bounegta , Mustapha Khelifi , Mohammed Beladgham
The main goal of this study is to develop and evaluate a novel 3D U-Net++ convolutional neural network for accurate segmentation of glioma sub regions in MRI scans, aiming to enhance surgical planning, targeted radiotherapy, and patient safety. Precise segmentation of glioma sub-regions is a persistent challenge in neuro-oncology due to substantial morphological variability across patients. To address this, we introduce an automatic segmentation model based on a 3D U-Net++ architecture with dense skip connections, which improves spatial feature extraction and the delineation of tumor boundaries. Utilizing volumetric data from the BraTS 2020 benchmark, the model automatically segments three clinically relevant substructures: tumor core, the enhancing tumor, and whole tumor. The integration of dense connections with 3D convolutional layers facilitates the detection of subtle tissue variations, including necrosis and edema. Quantitative evaluation demonstrates that the proposed 3D U-Net++ surpasses conventional architectures such as standard U-Net and DeepMedic in Dice coefficient, sensitivity, and specificity, yielding more homogeneous and continuous segmentations while reducing manual and semi-automatic annotation efforts. This approach supports advanced clinical decision making and workflow automation, and offers potential for application to other tumor types or integration into real-time clinical practice.
Volume: 42
Issue: 3
Page: 798-808
Publish at: 2026-06-10

Deep learning in cryptanalysis a comprehensive review of techniques, applications, challenges, and future trajectories

10.11591/ijeecs.v42.i3.pp846-855
Oussama Noui , Amine Barkat
The integration of deep learning (DL) into cryptanalysis represents a paradigm shift, challenging traditional mathematical approaches and unlocking novel attack vectors against cryptographic algorithms and implementations. This comprehensive review synthesizes findings from recent pivotal publications to provide an in-depth analysis of the current state-of-the-art, methodologies, empirical successes, fundamental limitations, and future potential of DL in cryptanalysis. We focus extensively on two primary domains DL-based side-channel analysis (DL-SCA) and DL-enhanced cryptanalysis of symmetric primitives. The review meticulously examines advancements in attack efficiency (reducing the number of traces/queries), robustness against sophisticated countermeasures, automated feature extraction, and the nascent exploration of theoretical foundations. While DL demonstrates remarkable capabilities in automating complex pattern recognition critical to cryptanalysis, significant challenges persist, including the “black-box” nature of models, data dependency, scalability to full cryptographic primitives, and the critical need for explainability and theoretical grounding. This review serves as a foundational resource for researchers and practitioners navigating this rapidly evolving intersection of artificial intelligence and cryptography.
Volume: 42
Issue: 3
Page: 846-855
Publish at: 2026-06-10

Design procedure and digital control of a DCM flyback converter: component sizing and experimental validation

10.11591/ijeecs.v42.i3.pp688-698
Nabil Abouchabana , Mohammed Benmiloud , Khaled Ameur , Aboubakeur Hadjaissa
Flyback converters are widely used in low-power switch-mode power supplies (SMPS) due to their simple structure, galvanic isolation capability, and cost effectiveness. This paper presents a systematic design methodology and digital Proportional–Integral (PI) control implementation for a discontinuous conduc tion mode (DCM) flyback converter using a dSPACE 1104 control platform. The proposed approach integrates magnetic component sizing, semiconductor stress evaluation, and RCD snubber design into a unified workflow. A 30 W prototype operating at 30 kHz with an input range of 20–30 V and a regulated 12 V output was developed and experimentally validated. The digital PI con troller was tuned using Takahashi’s method to ensure stable voltage regulation. Experimental results demonstrate proper DCM operation and stable output regulation under input voltage variation (20–30 V), load variation (48 Ω–12 Ω), and reference changes (10–14 V). The measured efficiency exceeded 90% at nominal operating conditions. The results confirm the effectiveness of the proposed design methodology for low-power isolated DC–DC applications.
Volume: 42
Issue: 3
Page: 688-698
Publish at: 2026-06-10
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