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All Journal Bulletin of Electrical Engineering and Informatics Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Journal of Applied Data Sciences International Journal of Robotics and Control Systems Sewagati Jurnal Penyakit Dalam Indonesia Journal of Medicine and Health Technology Bioma : Jurnal Biologi dan Pembelajaran Biologi
Yuri Pamungkas
Institut Teknologi Sepuluh Nopember
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Neuroscience Public Health Social Sciences Other

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Impact of Hyperparameter Tuning on ResNet-UNet Models for Enhanced Brain Tumor Segmentation in MRI Scans Pamungkas, Yuri; Triandini, Evi; Yunanto, Wawan; Thwe, Yamin
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.v5i2.1802

Abstract

Brain tumor segmentation in MRI scans is a crucial task in medical imaging, enabling early diagnosis and treatment planning. However, accurately segmenting tumors remains a challenge due to variations in tumor shape, size, and intensity. This study proposes a ResNet-UNet-based segmentation model using LGG dataset (from 110 patients), optimized through hyperparameter tuning to enhance segmentation performance and computational efficiency. The proposed model integrates different ResNet architectures (ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152) with UNet, evaluating their performance under various learning rates (0.01, 0.001, 0.0001), optimizer types (Adam, SGD, RMSProp), and activation functions (Sigmoid). The methodology involves training and evaluating each model using Loss Function, Mean Intersection over Union (mIoU), Dice Similarity Coefficient (DSC), and Iterations per Second as performance metrics. Experiments were conducted on MRI brain tumor datasets to assess the impact of hyperparameter tuning on model performance. Results show that lower learning rates (0.0001 and 0.001) improve segmentation accuracy, while Adam and RMSProp outperform SGD in minimizing segmentation errors. Deeper models (ResNet50, ResNet101, and ResNet152) achieve the highest mIoU (up to 0.902) and DSC (up to 0.928), but at the cost of slower inference speeds. ResNet50 and ResNet34 with RMSProp or Adam provide an optimal trade-off between accuracy and computational efficiency. In conclusion, hyperparameter tuning significantly impacts MRI segmentation performance, and selecting an appropriate learning rate, optimizer, and model depth is crucial for achieving high segmentation accuracy with minimal computational cost.
Enhancing Diabetic Retinopathy Classification in Fundus Images using CNN Architectures and Oversampling Technique Pamungkas, Yuri; Triandini, Evi; Yunanto, Wawan; Thwe, Yamin
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25331

Abstract

Diabetic Retinopathy (DR) is a severe complication of diabetes mellitus that affects the retinal blood vessels and is a leading cause of blindness in productive-age individuals. The global increase in diabetes prevalence requires an effective DR classification system for early detection. This study aims to develop a DR classification system using several CNN architectures, such as EfficientNet-B4, ResNet-50, DenseNet-201, Xception, and Inception-ResNet-v2, with the application of the SMOTE oversampling technique to address data class imbalance. The dataset used is APTOS 2019, which has an unbalanced class distribution. Two scenarios were tested, the first without data balancing and the second with SMOTE implementation. The test results show that in the first scenario, Xception achieved the highest accuracy at 80.61%, but model performance was still limited due to majority class dominance. The application of SMOTE in the second scenario significantly improved model accuracy, with EfficientNet-B4 achieving the highest accuracy of 97.78%. Additionally, precision and recall increased dramatically in the second scenario, demonstrating SMOTE's effectiveness in enhancing the model's ability to detect minority classes and reduce prediction errors. DenseNet-201 achieved the highest precision at 99.28%, while Inception-ResNet-v2 recorded the highest recall at 98.57%. Overall, this study proves that the SMOTE method effectively addresses class imbalance in the fundus dataset and significantly improves CNN model performance. Although data balancing can help improve model quality by dealing with data imbalances, it comes at a higher computational cost. Using data balancing techniques with SMOTE significantly increased the iteration time per round on all tested CNN architectures.
Application of 3D Printing Technology for Medical Purposes: A State of the Art Pamungkas, Yuri; Kuswanto, Djoko; Syafira Eljatin, Dwinka; Nugroho Njoto, Edwin
Journal of Medicine and Health Technology Vol. 2 No. 1 (2025)
Publisher : Direktorat Riset dan Pengabdian Kepada Masyarakat, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j30466865.v2i1.2255

Abstract

Introduction: The application of 3D printing technology in healthcare has revolutionized various medical practices, allowing for personalized solutions tailored to individual patient needs. This study explores the current state of 3D printing in medical applications, highlighting its benefits and challenges. Method: A comprehensive review of existing literature was conducted, focusing on the utilization of 3D printing for creating anatomical models, prosthetics, and bioprinting of living tissues. The analysis included a survey of various additive manufacturing techniques, such as Selective Laser Sintering (SLS) and Stereolithography (SLA), to assess their effectiveness in medical contexts. Results: The findings indicate that 3D printing enhances surgical planning by providing accurate anatomical models, thereby improving surgical outcomes. Additionally, the technology facilitates the production of custom implants and prosthetics, leading to better integration with patients' anatomy. Bioprinting has shown promise in developing artificial organs and regenerative therapies, significantly impacting transplant medicine. Discussion: While 3D printing offers substantial advantages, challenges such as regulatory hurdles, ethical considerations, and the need for standardized practices remain. The technology's potential in personalized medicine is vast, suggesting a future where individualized treatments are commonplace. Continued research and collaboration among medical professionals, technologists, and regulatory bodies are essential to address these challenges and optimize the benefits of 3D printing in healthcare. This study underscores the transformative impact of 3D printing in medicine and its potential to enhance patient care and outcomes across various applications.
Hyperparameter Tuning of EfficientNet Method for Optimization of Malaria Detection System Based on Red Blood Cell Image Pamungkas, Yuri; Eljatin, Dwinka Syafira
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2257

Abstract

Nowadays, malaria has become an infectious disease with a high mortality rate. One way to detect malaria is through microscopic examination of blood preparations, which is done by experts and often takes a long time. With the development of deep learning technology, the observation of blood cell images infected with malaria can be more easily done. Therefore, this study proposes a red blood cell image-based malaria detection system using the EfficientNet method with hyperparameter tuning. There are three parameters which are learning rate, activation function, and optimiser. The learning rate used is 0.01 and 0.001, while the activation functions used are ReLU and Tanh. In addition, the optimisers used include Adam, SGD, and RMSProp. In the implementation, the cell image dataset from the NIH repository was pre-processed such as resizing, rotating, filtering, and data augmentation. Then the data is trained and tested on several EfficientNet models (B0, B1, B3, B5, and B7) and their performance values are compared. Based on the test results, EfficientNet-B5 and B7 models showed the best performance compared to other EfficientNet models. The most optimal system test results are when the EfficientNet B5 model is used with a learning rate of 0.001, ReLU activation function, and Adam optimiser, with values of 97.69% (accuracy), 98.36% (precision), and 97.03% (recall). This research has proven that proper model selection and hyperparameter tuning can maximise the performance of cell image-based malaria detection system. The development of this EfficientNet-based diagnostic method is more sensitive and specific in malaria detection using RBCs.
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.
Exploring the Determinants of User Acceptance for the Digital Diary Application in Type 1 Diabetes Management: A Structural Equation Modeling Approach Triandini, Evi; Permana, Putu Adi Guna; Hanief, Shofwan; Kuswanto, Djoko; Pamungkas, Yuri; Perwitasari, Rayi Kurnia; Hisbiyah, Yuni; Rochmah, Nur; Faizi, Muhammad
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.767

Abstract

Effective management of Type 1 Diabetes (T1D), especially in children, requires continuous monitoring and care. Digital health applications have become vital in supporting routine T1D management, including insulin delivery, glucose monitoring, nutrition, and physical activity tracking. This study investigates factors influencing user acceptance of a digital diary app designed for children with T1D and their families. Using an extended Technology Acceptance Model incorporating Trust, Perceived Risk, Perceived Enjoyment, and Social Influence, a survey was conducted with 114 participants, including parents, physicians, and dietitians. Data were analyzed using Partial Least Squares Structural Equation Modeling. Findings indicate that perceived usefulness, trust, and social influence significantly affect users' attitudes and intentions to use the app, through the accepted hypothesis that considered path coefficients and p-values. Conversely, hypothesis that shows relation between perceived ease of use, enjoyment, and risk toward intention were rejected, showing unsignificant relations toward user intention to use. Furthermore, this study recommends prioritizing robust security features, fostering user trust, and engaging social networks to enhance digital health adoption in pediatric care. Future research should further explore the roles of perceived risk and enjoyment in sustaining long-term engagement
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.
The Emerging Role of Artificial Intelligence in Identifying Epileptogenic Zone: A Systematic Literature Review Pamungkas, Yuri; Radiansyah, Riva Satya; Pratasik, Stralen; Krisnanda, Made; Derek, Natan
Journal of Robotics and Control (JRC) Vol. 6 No. 5 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i5.27281

Abstract

Identifying epileptogenic zones (EZs) is a crucial step in the pre-surgical evaluation of drug-resistant epilepsy patients. Conventional methods, including EEG/SEEG visual inspection and neurofunctional imaging, often face challenges in accuracy, reproducibility, and subjectivity. The rapid development of artificial intelligence (AI) technologies in signal processing and neuroscience has enabled their growing use in detecting epileptogenic zones. This systematic review aims to explore recent developments in AI applications for localizing epileptogenic zones, focusing on algorithm types, dataset characteristics, and performance outcomes. A comprehensive literature search was conducted in 2025 across databases such as ScienceDirect, Springer Nature, and IEEE Xplore using relevant keyword combinations. The study selection followed PRISMA guidelines, resulting in 34 scientific articles published between 2020 and 2024. Extracted data included AI methods, algorithm types, dataset modalities, and performance metrics (accuracy, AUC, sensitivity, and F1-score). Results showed that deep learning was the most used approach (44%), followed by machine learning (35%), multi-methods (18%), and knowledge-based systems (3%). CNN and ANN were the most commonly applied algorithms, particularly in scalp EEG and SEEG-based studies. Datasets ranged from public sources (Bonn, CHB-MIT) to high-resolution clinical SEEG recordings. Multimodal and hybrid models demonstrated superior performance, with several studies achieving accuracy rates above 98%. This review confirms that AI (especially deep learning with SEEG and multimodal integration) has strong potential to improve the precision, efficiency, and scalability of EZ detection. To facilitate clinical adoption, future research should focus on standardizing data pipelines, validating AI models in real-world settings, and developing explainable, ethically responsible AI systems.
Transforming EEG into Scalable Neurotechnology: Advances, Frontiers, and Future Directions Pamungkas, Yuri; Triandini, Evi; Forca, Adrian Jaleco; Sangsawang, Thosporn; Karim, Abdul
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.v7i3.13824

Abstract

Electroencephalography (EEG) is a key neurotechnology that enables non-invasive, high-temporal resolution monitoring of brain activity. This review examines recent advancements in EEG-based neuroscience from 2021 to 2025, with a focus on applications in neurodegenerative disease diagnosis, cognitive assessment, emotion recognition, and brain-computer interface (BCI) development. Twenty peer-reviewed studies were selected using predefined inclusion criteria, emphasizing the use of machine learning on EEG data. Each study was assessed based on EEG settings, feature extraction, classification models, and outcomes. Emerging trends show increased adoption of advanced computational techniques such as deep learning, capsule networks, and explainable AI for tasks like seizure prediction and psychiatric classification. Applications have expanded to real-world domains including neuromarketing, emotion-aware architecture, and driver alertness systems. However, methodological inconsistencies (ranging from varied preprocessing protocols to inconsistent performance metrics) pose significant challenges to reproducibility and real-world deployment. Technical limitations such as inter-subject variability, low spatial resolution, and artifact contamination were found to negatively impact model accuracy and generalizability. Moreover, most studies lacked transparency regarding bias mitigation, dataset diversity, and ethical safeguards such as data privacy and model interpretability. Future EEG research must integrate multimodal data (e.g., EEG-fNIRS), embrace real-time edge processing, adopt federated learning frameworks, and prioritize personalized, explainable models. Greater emphasis on reproducibility and ethical standards is essential for the clinical translation of EEG-based technologies. This review highlights EEG’s expanding role in neuroscience and emphasizes the need for rigorous, ethically grounded innovation.
Co-Authors Abdul Karim Abdurahman Abdurrahman Achmad Syaifudin Adhi Dharma Wibawa Adhi Dharma Wibawa, Adhi Dharma Alfonsus Haryo Sangaji Aung, Myo Min Balqis, Dayana Satira Cahya, Meiliana Dwi Derek, Natan Dian Puspita Hapsari Djaputra, Edith Maria Dwinka Syafira Eljatin Elfrida Ratnawati Evi Triandini Fadli, Sonny Faiqoh, Elok Nur Fitriani, Fatimah Nur Forca, Adrian Jaleco Ginting, Tsamarah Amelia Putri Hashim, Uda Haykal, Muhammad Nazhif Haykal, Muhammad. Najib Hedianto, Tri Hidayah, Rizka Nurul Hisbiyah, Yuni Imam Susilo Indriani, Ratri Dwi Indriastuti, Endah Indriastuti, Endah Karimah, Rumman Kendenan, Valentino Kuswanto, Djoko Larasati, Alya Puti Made Krisnanda Meiliana Dwi Cahya Meiliana Dwi Cahya Muhammad Faizi, Muhammad Nakkliang, Kanittha Njoto, Edwin Nugroho Nugroho Njoto, Edwin Nur Rochmah, Nur Nur, Rossa Alfi Padma Nyoman Crisnapati Parlindungan, Putra Gelar Perwitasari, Rayi Kurnia Pratasik, Stralen Putra, Gumilar Fardhani Ami Putri Alief Siswanto Putri, Atina I.W. Putri, Atina Irani Wira Putri, Ziyan Nadia Putu Adi Guna Permana Rachmadiana, Josephine Larissa Radiansyah, Riva Satya Ramadani, Muhammad Rifqi Nur Rangkuti, Rahmah Yasinta Ridhoi, Ahmad Risald, Randi Achtiar Sain, Anabela Aulia Sakina Sangsawang, Thosporn Shofwan Hanief Supeno Mardi Susiki Nugroho, Supeno Mardi Syafira Eljatin, Dwinka Syulthoni, Zain Budi Thwe, Yamin Uda, Muhammad Nur Afnan Wawan Yunanto Yulan, Gao