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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 25 Documents
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Search results for , issue "Vol 7 No 2 (2025): April" : 25 Documents clear
Swarm Intelligence-Based Functional Link Fuzzy Neural Estimator for Software Development Effort Estimation Benala, Tirimula Rao; Kaushik, Anupama; Dehuri, Satchidananda
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.492

Abstract

Accurate Software Development Effort Estimation (SDEE) is pivotal for effective project management, significantly impacting resource allocation and the overall success of software projects. This paper introduces the Swarm Intelligence-Based Functional Link Fuzzy Neural Estimator (SFNE), a novel computational intelligence model designed to enhance estimation accuracy by integrating multiple advanced methodologies. The SFNE framework employs the QUICK algorithm for dataset optimization, effectively minimizing noise and redundancy. A Functional Link Artificial Neural Network (FLANN) captures complex nonlinear relationships within the data, while Interval Type-2 Fuzzy Logic Systems (IT2FLS) address inherent data uncertainties. Additionally, Particle Swarm Optimization (PSO) is applied to fine-tune model parameters, improving prediction precision. Empirical evaluations were conducted using six benchmark datasets from the PROMISE repository. The results demonstrate that the SFNE model significantly outperforms existing models across key metrics, including Mean Magnitude of Relative Error (MMRE), Median Magnitude of Relative Error (MdMRE), and Prediction at 0.25 (PRED(0.25)). Notably, SFNE achieved a predictive accuracy of 99.983% on the DesharnaisL3 dataset and an MMRE of 2.87×10⁻⁵ on the DesharnaisL1 dataset. These findings underscore the robustness and adaptability of SFNE in addressing the limitations of traditional SDEE methods, particularly in managing data scarcity and uncertainty. The proposed SFNE model establishes a new benchmark for SDEE accuracy and demonstrates substantial potential for practical application in real-world software engineering projects. Future research will explore integrating additional computational intelligence techniques, such as deep learning and reinforcement learning, and developing automated tools to advance SDEE practices further. These advancements contribute to more reliable and efficient software project management, facilitating real-time effort estimation and informed decision-making in the software industry.
The Enhancing Diabetes Prediction Accuracy Using Random Forest and XGBoost with PSO and GA-Based Feature Selection Dzira Naufia Jawza; Mazdadi, Muhammad Itqan; Farmadi, Andi; Saragih, Triando Hamonangan; Kartini, Dwi; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.626

Abstract

Diabetes represents a global health concern classified as a non-communicable disease, impacting more than 422 million people worldwide, with the number expected to increase each year. This study aims to evaluate the performance of the Random Forest and Extreme Gradient Boosting (XGBoost) classification algorithms on the diabetes disease dataset taken from Kaggle. To improve prediction accuracy, feature selection was carried out using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which are expected to filter the most relevant features. The study results showed that the Random Forest model without feature selection yielded an Area Under Curve (AUC) value of 0.8120, while XGBoost achieved an AUC of 0.7666. After applying feature selection with PSO, the AUC increased to 0.8582 for Random Forest and 0.8250 for XGBoost. The use of feature selection with GA gave better results, with an AUC of 0.8612 for Random Forest and 0.8351 for XGBoost. These results indicate that the increase in accuracy after feature selection using PSO ranges from 5.7% to 7.6%, while the increase with GA ranges from 6.1% to 8.9%, with GA providing more significant results. This study contributes to improving the accuracy of diabetes disease classification, which is expected to support the diagnosis process more quickly and accurately.
Analysis of Differences in Image Quality and Anatomical Information of Head CT Scan Examination in Non-Hemorrhagic Stroke Cases Using Sinogram Affirmed Iterative Reconstruction (SAFIRE) Samudra, Alan; Fitriana, Lutfatul; Hidayat, Fathur Rachman; Wibowo, Kusnanto Mukti; Ariesma Githa Giovany; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.629

Abstract

SAFIRE should be utilized to its full potential, as this innovative image reconstruction algorithm can significantly reduce image noise without loss of sharpness, preserving image quality and anatomical information. This is particularly important in the case of non-hemorrhagic stroke, where image noise can obscure small lesions, potentially leading to misdiagnosis and inappropriate treatment. SAFIRE has five variations of strength, making it essential to identify the most optimal SAFIRE Strength for head CT Scan examinations in non-hemorrhagic stroke cases. The aim of this study is to determine differences in image quality and anatomical information in head CT Scan of non-hemorrhagic stroke cases using SAFIRE variations to identify the most optimal SAFIRE Strength. This experimental quantitative study involved a sample of 30 patients, with each case reconstructed using five SAFIRE Strength variations. Image quality was assessed using the IndoQCT application, while anatomical information was evaluated through the visual grading analysis method by three radiologists. Image quality data were analyzed using the Friedman statistical test, which resulted in a p-value of 0.000 (p < 0.05), indicating significant differences among the SAFIRE Strength variations. Similarly, anatomical information data were analyzed using the Kruskal-Wallis statistical test, yielding a p-value of 0.000 (p < 0.05), confirming significant differences across the variations. The results of the study showed that there are significant differences in image quality and anatomical information among the five SAFIRE Strength variations. SAFIRE Strength 3 was identified as the most optimal for head CT Scan examinations in non-hemorrhagic stroke cases, as it produces images with minimal noise and higher detail, providing clearer anatomical information compared to the other SAFIRE Strength variations.
Comparative Analysis of Feature Extraction Techniques for Facial Paralysis Classification Himawan, Salamet Nur; Suheryadi, Adi; Cahyanto, Kurnia Adi; Sitanggang, Filemon; Pamungkas, Kiki Adi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.645

Abstract

Facial paralysis significantly affects a person's ability to communicate and perform essential functions. Facial paralysis classification plays a vital role in the diagnosis and monitoring of facial disorders. Traditional diagnostic methods often rely on subjective evaluations, leading to inconsistent outcomes. The aim of this study is to evaluate and compare various feature extraction techniques to enhance the accuracy and efficiency of facial paralysis classification. The primary contribution of this research lies in its comprehensive analysis of texture-based (Local Binary Patterns, Histogram of Oriented Gradients, Gabor filters) and geometric feature extraction methods, providing insights into their respective strengths and limitations for facial paralysis detection. This study utilizes the YouTube Facial Palsy (YFP) dataset, comprising annotated images of paralyzed and non-paralyzed faces. Preprocessing included resizing images to 128x128 pixels to standardize inputs. Feature extraction methods were applied to the dataset, and the extracted features were classified using machine learning algorithms, including Support Vector Machines (SVM), Random Forest (RF), and k-Nearest Neighbors (KNN). Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The best-performing method achieved an accuracy of 85% using HOG features combined with KNN. The findings highlight that texture-based methods, particularly HOG, excel in capturing subtle asymmetries, while geometric features offer computational efficiency and interpretability with fewer extracted features. This study underscores the importance of selecting suitable feature extraction methods based on task requirements, and emphasizes the potential of hybrid approaches to leverage the strengths of different methods. Future research should explore advanced geometric descriptors and integrate hybrid models to enhance clinical applicability
Advancing Genomic Diagnostics: Fast Fourier Transform Optimization and Machine Learning in Huntington’s Disease Detection C, Saravanakumar; S, Marirajan; A, Pandian; K, Durgadevi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.650

Abstract

Optimizing the Fast Fourier Transform (FFT) for genomic data analysis offers a significant advancement in addressing challenges related to sequential input processing and computational efficiency. By integrating advanced signal processing techniques such as Infinite Impulse Response (IIR) filtering, the proposed approach effectively identifies spectral characteristics and dominant frequencies in DNA sequences. This framework demonstrates improved accuracy and reduced computational overhead, making it highly suitable for large-scale and real-time genomic applications. Machine learning models were employed to classify Huntington’s Disease (HD)-associated and normal DNA sequences, using spectral features as predictive markers. Among the models evaluated, K-Nearest Neighbors (KNN) achieved perfect scores across all performance metrics, including Classification Accuracy (CA), Area Under the Curve (AUC), Precision, Recall, Matthews Correlation Coefficient (MCC) and F1 Score. Support Vector Machine (SVM) and Neural Networks also delivered competitive results, emphasizing the effectiveness of combining signal processing with machine learning for medical diagnostics and genomic studies. The computational efficiency of the proposed FFT algorithm was validated using 2,300 genomic sequences, with 90% demonstrating enhanced processing speeds compared to traditional methods. These improvements were particularly notable for longer sequences, showcasing the algorithm’s capability in high-throughput genomic analysis. This approach is particularly impactful for investigating complex conditions like Huntington’s disease, where rapid and accurate identification of genetic markers is essential. This work underscores the potential of integrating FFT optimization with machine learning to revolutionize genomic data processing and disease detection. Beyond advancing computational genomics, the proposed methodology offers a foundation for broader bioinformatics applications, including the analysis of other genetic disorders and real-time clinical diagnostics, contributing to the evolution of precision medicine.
Optimizing Support Vector Machine for Avocado Ripeness Classification Using Moth Flame Optimization Crisannaufal, Kemal; Fawwaz Al Maki, Wikky
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.652

Abstract

Avocado is a fruit from Mexico and Central America that is widely distributed worldwide for production and consumption. In avocados, ripeness is crucial because it is the primary factor consumers consider, significantly influencing their purchasing decisions. The manual ripeness selection is inefficient and inconsistent, so the classification system is essential for determining ripeness due to its effectiveness and efficiency compared to manual selection. In this study, we aim to develop a model that can classify avocado ripeness using machine learning with optimization. The data consists of avocado images categorized into five ripeness stages: underripe, breaking, ripe (first stage), ripe (second stage), and overripe. We utilize a Support Vector Machine (SVM) for the classification. Instead of manually choosing the model’s hyperparameters, we use Moth Flame Optimization (MFO) to optimize the SVM hyperparameters. The MFO ensures that the proposed model has optimal performance. For the input of SVM, we extract the HSV, GLCM, and HOG and apply PCA to the data. In this study, we use three SVM kernels: RBF, polynomial, and sigmoid. The MFO finds the model’s hyperparameters based on kernel requirements, including C, gamma, degree, and coef0. The MFO-SVM obtains optimal performance with an accuracy of 82.55%, 82.68%, and 81.23% for SVM kernel RBF, polynomial, and sigmoid, respectively. The results show that our proposed model demonstrates adequate performance in identifying the ripeness levels of avocados. The MFO increases model performance on all evaluation metrics compared to the baseline model and can be an excellent strategy to improve model performance.
Categorizing Crowd Emotions based on Cross Division Expressions and Anomalies Kothandapani, Manojkumar; L., Suji Helen
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.673

Abstract

The crowd emotion sensing is a critical element in surveillance and management of the crowd in different environments. With exploding populations, and developing nations, the crowd in urban cities mandate state of art surveillance methodologies involving continuous monitoring and reporting of criminal activities. The research article presents a novel technique to compute the spatial and temporal features obtained from the crowd environments and combine the novelty of neural networks for detecting the emotions of crowds with better accuracy and swiftness. The features are obtained from the continuous feed of surveillance videos typically categorized into the common features of human beings namely anger, sadness, disgust, surprise, fear, happiness and obviously neutrality. Such features are extracted after careful background separation which are typically difficult in crowded environments, using techniques namely SIFT, and FAST termed to be the visual descriptors. Once the features are extracted, spatial and temporal features are classified into individual and combined features as defined in the cross-division environment in order to portray the crowd dynamics and characteristics. Cross division environment computes the necessary features for identifying the anomalies in the crowded situations in a neural network, after a series of operations such as dimensionality reduction, and principal component analysis. From the semantic information, crowd behaviours are detected based on interactive features in a dynamic environment and the proposed technique has demonstrated effective results in terms of 98.9% accuracy in detecting especially violence in crowd datasets collected from UMN.
Model Group Decision Support System Based on Depression Anxiety Stress Scales Using Ordered Weighted Averaging Aggregation Method Wiharto, Wiharto; Putri, Della K.; Sihwi, Sari W.; Salamah, Umi; Suryani, Esti; Atina, Vihi; Utomo, Pradityo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.678

Abstract

Depression, anxiety, and stress are common psychological conditions often triggered by the pressures of daily life. Depression Anxiety Stress Scale (DASS), is a widely used tool for assessing the severity of these disorders, available in different versions such as the DASS-21 and DASS-42. In line with these findings, DASS-21 consists of 21 symptom items, categorized into three types of disorders, with seven items assigned to each. In contrast, the DASS-42 includes 42 symptom items, with 14 items allocated per disorder. Both versions serve as standardized tools for assessing the severity of depression, anxiety, and stress, and the different versions show that one item only affects one disorder. In practice, it can affect several disorders with different priorities. This condition increases the risk of subjective bias in a psychologist's decision-making, as personal experiences and perceptions may influence their assessments. Therefore, this study aims to develop a Group Decision Support System (GDSS) model that considers the preferences of several psychologists in determining the priority of disorders based on the DASS-42 and DASS-21 items. The model has been built using the psychologist's preference method for DASS-42 and DASS-21 in fuzzy form, then combined using the Ordered Weighted Averaging (OWA) method to produce one decision. The alignment of top-priority items between GDSS and DASS was assessed as part of the evaluation. The results show a high degree of similarity, with GDSS matching 16 out of 21 symptom items in DASS-21 and 35 out of 42 items in DASS-42. The GDSS model can accommodate the preferences of decision-makers in providing weighting of the influence on each item in the DASS-21 and DASS-42, thereby providing more objective decisions.
Hybrid Sign Language Recognition Framework Leveraging MobileNetV3, Mult-Head Self Attention and LightGBM Kumar, Hemant; Sachan, Rishabh; Tiwari, Mamta; Katiyar, Amit Kumar; Awasthi, Namita; Mamoria, Puspha
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.685

Abstract

Sign-language recognition (SLR) plays a pivotal role in enhancing communication accessibility and fostering the inclusion of deaf communities. Despite significant advancements in SLR systems, challenges such as variability in sign language gestures, the need for real-time processing, and the complexity of capturing spatiotemporal dependencies remain unresolved. This study aims to address these limitations by proposing an advanced framework that integrates deep learning and machine learning techniques to optimize sign language recognition systems, with a focus on the Indian Sign Language (ISL) dataset. The framework leverages MobileNetV3 for feature extraction, which is selected after rigorous evaluation against VGG16, ResNet50, and EfficientNet-B0. MobileNetV3 demonstrates superior accuracy and efficiency, making it optimal for this task. To enhance the model's ability to capture complex dependencies and contextual information, multi-head self-attention (MHSA) was incorporated. This process enriches the extracted features, enabling a better understanding of sign language gestures. Finally, LightGBM, a gradient-boosting algorithm that is efficient for large-scale datasets, was employed for classification. The proposed framework achieved remarkable results, with a test accuracy of 98.42%, precision of 98.19%, recall of 98.81%, and an F1-score of 98.15%. The integration of MobileNetV3, MHSA, and LightGBM offers a robust and adaptable solution that outperforms the existing methods, demonstrating its potential for real-world deployment. In conclusion, this study advances precise and accessible communication technologies for deaf individuals, contributing to more inclusive and effective human-computer interaction systems. The proposed framework represents a significant step forward in SLR research by addressing the challenges of variability, real-time processing, and spatiotemporal dependency. Future work will expand the dataset to include more diverse gestures and environmental conditions and explore cross-lingual adaptations to enhance the model’s applicability and impact.
Deep Vision Transformer with Tasmanian Devil Optimization for Multiclass Paddy Disease Detection and Classification for Precision Agriculture Shanthi AL; Jamalpur, Bhavana; R, Vijayaganth; Venkatesh, Naramula
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.591

Abstract

Rice is the daily consumed crop all over the country and other parts of the world. Rice is cultivated in most of the states. Nevertheless, rice plant diseases deteriorate the quantity and quality of the crop. Rice plants are affected by various conditions, for example: sheath blight, foot rot, and so on, producing a loss in the farming yield. Therefore, earlier disease recognition in crops is important. Performing intelligent Farming is a hot zone of investigation to prevent more harm to crops. The extensive growth of Deep Learning (DL) makes it probable to attain the objective of disease recognition in crops. In this manuscript, we introduce a new Deep Vision Transformer with Tasmanian Devil Optimization for Multiclass Paddy Disease Detection and Classification (DViTTDO-MPDDC) technique for Precision Agriculture. The major intention of the DViTTDO-MPDDC technique focuses on the automatic classification and recognition of paddy plant diseases. To accomplish this, the DViTTDO-MPDDC technique uses the wiener filter (WF) technique for the noise removal process. Besides, the vision transformer (ViT) technique is used for feature extraction purposes. Additionally, the attention mechanism-based convolutional neural network with bidirectional long short-term memory (AM-CNN-BiLSTM) technique is used for the paddy disease detection process. Eventually, the TDO algorithm is exploited for the hyperparameter fine-tuning of the AM-CNN-BiLSTM model. To demonstrate the good classification outcome of the DViTTDO-MPDDC algorithm, a wide variety of models occurs on the benchmark database. The extensive comparable findings ensured the betterment of the DViTTDO-MPDDC method over the current methods.

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