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All Journal Bulletin of Electrical Engineering and Informatics Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Environmental and Toxicology Management (ETM)
Hashim, Uda
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Aerospace Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Environmental Science Mechanical Engineering Public Health

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Evaluating the Effectiveness of Alzheimer’s Detection Using GANs and Deep Convolutional Neural Networks (DCNNs) Pamungkas, Yuri; Syaifudin, Achmad; Crisnapati, Padma Nyoman; Hashim, Uda
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1855

Abstract

Alzheimer’s is a gradually worsening condition that damages the brain, making timely and precise diagnosis essential for better patient care and outcomes. However, existing detection methods using DCNNs are often hampered by the problem of class imbalance in datasets, particularly OASIS and ADNI, where some classes are underrepresented. This study proposes a novel approach integrating GANs with DCNNs to tackle class imbalance by creating synthetic samples for underrepresented categories. The primary focus of this research is demonstrating that using GANs for data augmentation can significantly strengthen DCNNs performance in Alzheimer's detection by balancing the data distribution across all classes. The proposed method involves training DCNNs with both original and GAN-generated data, with data partitioning of 80:10:10 for training/ validation/ testing. GANs are applied to generate new samples for underrepresented classes within the OASIS and ADNI datasets, ensuring balanced datasets for model training. The experimental results show that using GANs improves classification performance significantly. In the case of the OASIS dataset, the mean accuracy and F1 Score rose from 99.64% and 95.07% (without GANs) to 99.98% and 99.96% (with GANs). For the ADNI dataset, the average accuracy and F1 Score improved from 96.21% and 93.01% to 99.51% and 99.03% after applying GANs. Compared to existing methods, the proposed GANs + DCNNs model achieves higher accuracy and robustness in detecting various stages of Alzheimer's disease, particularly for minority classes. These findings confirm the effectiveness of GANs in improving DCNNs' performance for Alzheimer's detection, providing a promising framework for future diagnostic implementations.
Deep Learning Approach to Lung Cancer Detection Using the Hybrid VGG-GAN Architecture Pamungkas, Yuri; Kuswanto, Djoko; Syaifudin, Achmad; Triandini, Evi; Hapsari, Dian Puspita; Nakkliang, Kanittha; Uda, Muhammad Nur Afnan; Hashim, Uda
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.1923

Abstract

Lung cancer ranks among the primary contributors to cancer-related deaths globally, highlighting the need for accurate and efficient detection methods to enable early diagnosis. However, deep learning models such as VGG16 and VGG19, commonly used for CT scan image classification, often face challenges related to class imbalance, resulting in classification bias and reduced sensitivity to minority classes. This study contributes by proposing an integration of the VGG architecture and Generative Adversarial Networks (GANs) to improve lung cancer classification performance through balanced and realistic synthetic data augmentation. The proposed approach was evaluated using two datasets: the IQ-OTH/NCCD Dataset, which classifies patients into Benign, Malignant, and Normal categories based on clinical condition, and the Lung Cancer CT Scan Dataset, annotated with histopathological labels: Adenocarcinoma, Squamous Cell Carcinoma, Large Cell Carcinoma, and Normal. The method involves initial training of the VGG model without augmentation, followed by GAN-based data generation to balance class distribution. The experimental results show that, prior to augmentation, the models achieved relatively high overall accuracy, but with poor performance on minority classes (marked by low precision and F1-scores and FPR exceeding 8% in certain cases). After augmentation with GAN, all performance metrics improved dramatically and consistently across all classes, achieving near-perfect precision, TPR, F1-score, and overall accuracy of 99.99%, and FPR sharply reduced to around 0.001%. In conclusion, the integration of GAN and VGG proved effective in overcoming data imbalance and enhancing model generalization, making it a promising solution for AI-based lung cancer diagnostic systems.
A Comprehensive Review of EEGLAB for EEG Signal Processing: Prospects and Limitations Pamungkas, Yuri; Rangkuti, Rahmah Yasinta; Triandini, Evi; Nakkliang, Kanittha; Yunanto, Wawan; Uda, Muhammad Nur Afnan; Hashim, Uda
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.27084

Abstract

EEGLAB is a MATLAB-based software that is widely used for EEG signal processing due to its complete features, analysis flexibility, and active open-source community. This review aims to evaluate the use of EEGLAB based on 55 research articles published between 2020 and 2024, and analyze its prospects and limitations in EEG processing. The articles were obtained from reputable databases, namely ScienceDirect, IEEE Xplore, SpringerLink, PubMed, Taylor & Francis, and Emerald Insight, and have gone through a strict study selection stage based on eligibility criteria, topic relevance, and methodological quality. The review results show that EEGLAB is widely used for EEG data preprocessing such as filtering, ICA, artifact removal, and advanced analysis such as ERP, ERSP, brain connectivity, and activity source estimation. EEGLAB has bright prospects in the development of neuroinformatics technology, machine learning integration, multimodal analysis, and large-scale EEG analysis which is increasingly needed. However, EEGLAB still has significant limitations, including a high reliance on manual inspection in preprocessing, low spatial resolution in source modeling, limited multimodal integration, low computational efficiency for large-scale EEG data, and a high learning curve for new users. To overcome these limitations, future research is recommended to focus on developing more accurate automation methods, increasing the spatial resolution of source analysis, more efficient multimodal integration, high computational support, and implementing open science with a standardized EEG data format. This review provides a novel contribution by systematically mapping EEGLAB’s usage trends and pinpointing critical technical and methodological gaps that must be addressed for broader neurotechnology adoption.
Transfer Learning Models for Precision Medicine: A Review of Current Applications Pamungkas, Yuri; Aung, Myo Min; Yulan, Gao; Uda, Muhammad Nur Afnan; Hashim, Uda
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.14286

Abstract

In recent years, Transfer Learning (TL) models have demonstrated significant promise in advancing precision medicine by enabling the application of machine learning techniques to medical data with limited labeled information. TL overcomes the challenge of acquiring large, labeled datasets, which is often a limitation in medical fields. By leveraging knowledge from pre-trained models, TL offers a solution to improve diagnostic accuracy and decision-making processes in various healthcare domains, including medical imaging, disease classification, and genomics. The research contribution of this review is to systematically examine the current applications of TL models in precision medicine, providing insights into how these models have been successfully implemented to improve patient outcomes across different medical specialties. In this review, studies sourced from the Scopus database, all published in 2024 and selected for their "open access" availability, were analyzed. The research methods involved using TL techniques like fine-tuning, feature-based learning, and model-based transfer learning on diverse datasets. The results of the studies demonstrated that TL models significantly enhanced the accuracy of medical diagnoses, particularly in areas such as brain tumor detection, diabetic retinopathy, and COVID-19 detection. Furthermore, these models facilitated the classification of rare diseases, offering valuable contributions to personalized medicine. In conclusion, Transfer Learning has the potential to revolutionize precision medicine by providing cost-effective and scalable solutions for improving diagnostic capabilities and treatment personalization. The continued development and integration of TL models in clinical practice promise to further enhance the quality of patient care.
A mini review of electrochemical genosensor based biosensor diagnostic system for infectious diseases Parmin, Nor Azizah; Hashim, Uda; Gopinath, Subash C.B.; Dahalan, Farrah Aini; Voon, C.H.; Uda, M.N.A.; Afnan Uda, M.N.; Rejali, Zulida; Afzan, Amilia; Jaapar, F. Nadhirah; Halim, F. Syakirah
Environmental and Toxicology Management Vol. 1 No. 1 (2021): Developing and implementing green technologies for environmental management
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (172.108 KB) | DOI: 10.33086/etm.v1i1.2038

Abstract

The quest for alternative methods is driven by the need to provide expertise in real time in biological fields such as medicine, pathogenic bacteria and viruses identification, food protection, and quality control. Polymerase Chain Reaction (PCR) and Enzyme Linked Immunosorbent Assay (ELISA) are examples of traditional methods that have some limitations and lengthy procedures. Biosensors are the most appealing option because they provide easy, dependable, fast, and selective detection systems compared to conventional methods. This review provides an overview of electrochemical genosensor based biosensor diagnostic system for infectious diseases detection as well as their applications, demonstrating their utility as a fast and responsive tool for detecting pathogenic bacteria, viruses, GMOs, and human diseases.
A Review of EEG Applications in Neuromarketing: Methods, Insights, and Future Directions Pamungkas, Yuri; Thwe, Yamin; Karim, Abdul; Hashim, Uda
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14375

Abstract

EEG is increasingly applied in neuromarketing as it provides direct insights into consumer cognition and emotion beyond traditional self-report measures. However, challenges such as small samples, low ecological validity, and methodological limitations hinder its broader real-world application. The research contribution is a comprehensive synthesis of 40 empirical studies that examine EEG applications in neuromarketing, highlighting methodological approaches, analytical techniques, key insights, and persistent gaps that define the current state of the field. This review applied a structured comparative method by extracting and analyzing details from published EEG-based neuromarketing studies, including sample characteristics, device specifications, stimuli types, analytical techniques, and outcomes. The data were organized into a review table and further examined for patterns, strengths, limitations, and emerging opportunities. The results reveal that EEG can reliably classify consumer preferences when paired with deep learning models, while EEG indices such as neural synchrony and frontal alpha asymmetry predict advertising effectiveness and purchase intention. Emotional and attentional processes were consistently reflected in ERP components, and multimodal integration with physiological and behavioral data improved predictive validity. Nonetheless, most studies relied on small, homogeneous samples and static laboratory stimuli, limiting generalizability. In conclusion, EEG holds strong potential for advancing neuromarketing research and practice, yet future work must address scalability, cross-cultural validation, and ecological realism to fully harness its promise.
Alzheimer's disease detection based on MR images using the quad convolutional layers CNN approach Pamungkas, Yuri; Syaifudin, Achmad; Yunanto, Wawan; Hashim, Uda
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10304

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder requiring early and accurate detection for effective intervention. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs), have shown promise in medical image classification. However, conventional CNN models often suffer from high computational complexity and inefficiency in handling imbalanced datasets. This study proposes a quad convolutional layers-CNN (QCL-CNN) for Alzheimer’s disease detection using magnetic resonance images (MRI) scans from the open access series of imaging studies (OASIS) dataset, which includes four dementia stages, non-dementia, very mild dementia, mild dementia, and moderate dementia. The QCL-CNN model employs four sequential convolutional layers for enhanced multi-level feature extraction, ensuring efficient classification while minimizing computational overhead. The experimental results demonstrate that QCL-CNN outperforms traditional CNN architectures, achieving an accuracy of 99.90%, recall of 99.89%, specificity of 99.93%, and an F1-score of 99.52%. The model surpasses VGG19, Xception, ResNet50, and DenseNet201 while maintaining a significantly lower parameter count (4.2M), making it computationally efficient. These findings confirm that network optimization is more crucial than model depth, ensuring robust performance even with fewer layers. Future research should explore multi-modal imaging, class balancing techniques, and real-world clinical validation to further improve the model’s diagnostic capabilities. The QCL-CNN model offers a promising artificial intelligence (AI)-powered approach for early Alzheimer’s detection, enabling precise, and efficient medical diagnosis.
Co-Authors Abdul Karim Achmad Syaifudin Afnan Uda, M.N. Afzan, Amilia Aung, Myo Min Dahalan, Farrah Aini Dian Puspita Hapsari Evi Triandini Gopinath, Subash C.B. Halim, F. Syakirah Jaapar, F. Nadhirah Kuswanto, Djoko Nakkliang, Kanittha Padma Nyoman Crisnapati Parmin, Nor Azizah Rangkuti, Rahmah Yasinta Rejali, Zulida Thwe, Yamin Uda, M.N.A. Uda, Muhammad Nur Afnan Voon, C.H. Wawan Yunanto Yulan, Gao Yuri Pamungkas