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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 20 Documents
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Search results for , issue "Vol 6 No 4 (2024): October" : 20 Documents clear
Application Of SMOTE To Address Class Imbalance In Diabetes Disease Classification Utilizing C5.0, Random Forest, And SVM M. Khairul Rezki; Mazdadi, Muhammad Itqan; Indriani, Fatma; Muliadi, Muliadi; Saragih, Triando Hamonangan; Athavale, Vijay Annant
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.434

Abstract

The implementation of SMOTE to tackle class imbalance in classification frequently results in suboptimal outcomes, owing to the intricacy of the dataset and the multitude of attributes at play. Consequently, alternative classification models were explored through experimentation to gauge their precision. This research aims to compare the precision of C5.0, Random Forest, and SVM classification models both with and without SMOTE. The methodology encompasses dataset selection, an overview of classification algorithms (C5.0, Random Forest, SVM), SMOTE technique, validation via split validation, preprocessing involving min-max normalization, and execution evaluation utilizing confusion matrices and AUC analysis. The dataset was sourced by Kaggle, specifically to rectify class imbalance in a diabetes dataset using SMOTE, consisting of 768 instances, with 268 samples for diabetic cases and 500 samples for non-diabetic cases. Prior to SMOTE application, the classification precision for C5.0, Random Forest, and SVM were 0.714, 0.733, and 0.746 respectively, with corresponding AUC values of 0.745, 0.824, and 0.799. Post-SMOTE, the precision depicts for the same techniques were 0.603, 0.727, and 0.727, with AUC values of 0.734, 0.831, and 0.794 respectively. It can be inferred that there's minimal impact post-SMOTE across the three classification models due to potential overfitting on the dataset, leading to excessive reliance on synthesized data for minority classes, resulting in diminished model execution, precision, and AUC scores.
Bi-directional Long Short-Term Memory with Bird Mating Optimizer based Spectrum Sensing Technique for Cognitive Radio Networks M, Saraswathi; E, Logashanmugam
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.441

Abstract

Cognitive radio networks (CRN) enable the wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables for satisfying the QoS needs by reducing the energy utilization. In cognitive radio, the detection of principal user signals is crucial for secondary users in order to make the best use of available spectrum (CR). The problem with conventional spectrum sensing approaches is that they have a high rate of missed detections and false alarms, which makes it difficult to make effective use of the spectrum. In instruction to recover the correctness of the detection of free spectrum, deep learning-based spectrum sensing is employed. For resolving the drawbacks of traditional energy detection models, this paper presents a new spectral sensing technique for cognitive radio networks (SST-CRN). Recently published research in spectrum sensing has placed a high value on deep learning that is model-agnostic as a result of this. In particular, long-short term memory (LSTM) networks perform exceptionally well at extracting spatial and temporal information from input data in deep learning. The proposed model, Bidirectional Long Short-Term Memory (Bi-LSTM) with Bird Mating Optimization (BMO), makes it possible to create nonlinear threshold-based systems more quickly and easily than previously possible. The proposed Bi-LSTM with BMO technique involves two stages of operations namely offline and online. The offline stage creates the non-linear threshold value to detect energy. In addition, the online stage automated selects a decision function saved in offline stage for determining the existence of primary user. The experiments were carried out and the results were analyzed using the help of the RadioML2016.10b dataset. A greater spectrum detection performance over previous sensing models has been obtained using a combination of the B-LSTM model and the BMO model, it has been discovered.
Expert System for Pregnancy Risk Diagnosis Using Decision Tree and Dempster-Shafer Method Wiharto; Azizah, Setia Mukti; Hendrasuryawan, Brilyan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.448

Abstract

The high Maternal Mortality Rate (MMR) remains a severe concern in maternal healthcare. One of the reasons is the delay in recognizing early danger signs during pregnancy. To address this issue, there is a proposed solution in the form of developing an expert system aimed at swiftly and efficiently diagnosing pregnancy risks in pregnant women using the Decision Tree and Dempster Shafer methods. The Decision Tree method is employed for symptom classification, while Dempster Shafer provides confidence values for existing facts. This research collects data from the dataset, the Poedji Rochjati Score Card (KSPR), and qualitative data through expert interviews. From the collected data, knowledge acquisition processes are then carried out to extract knowledge using the ID3 Decision Tree and combine all symptoms from the gathered data. The processed data is then represented as a decision tree and assigned confidence values. The development of this expert system utilizes the Laravel framework with PHP language and MySQL database. System validation involves patients as participants and midwives as experts and testers. Testing was conducted on March 13 and 16, 2024, involving 16 patients at the Gatak Community Health Center. The system evaluation results show an accuracy rate of 93.75%. This value indicates that the system can operate effectively. Thus, it can be recommended for use in diagnosing pregnancy risks.
A Circular Ring Patch Antenna for Breast Cancer Detection Based on Return Loss and VSWR Ahmed, Md. Firoz; Kabir, M. Hasnat
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.456

Abstract

Breast cancer is a serious condition that affects women and requires timely identification. Various methods such as magnetic resonance imaging, mammography, digital mammography, and computer-aided detection are used for this purpose. However, these techniques have their drawbacks. To address this issue, a new approach is proposed and detailed in this paper. In this novel method, a circular patch antenna is employed to detect tumors in a breast phantom. The analysis of return loss and voltage standing wave ratio (VSWR) helps in identifying the presence of tumors. High-frequency structure simulator (HFSS) software is employed to design and simulate the antenna for an ultra-wideband (3.1 – 10.6 GHz) frequency of 5.2 GHz, along with a breast phantom with and without a tumor. The antenna is independently simulated on both the breast phantoms with and without tumors. Rogers RT/duroid 5880 (tm) dielectric material is employed to design the antenna, with overall dimensions of 30 × 20 × 0.8 mm3. It possesses a dielectric constant of 2.2, a tangent loss of 0.02, and a thickness of 0.8 mm. The ring slot and partial ground plane techniques are employed to increase the overall effectiveness of the antenna. The properties of the antenna, such as return loss and VSWR, change when simulated with and without a tumor. The presence of a tumor within the breast is clearly indicated by the alterations in return loss and VSWR. The antenna proposed exhibits remarkable efficacy in the detection of tumors owing to its inconspicuous features, straightforward design, petite dimensions, and ideal impedance matching.
Classification of Lung Disease in X-Ray Images Using Gray Level Co-Occurrence Matrix Method and Convolutional Neural Network Nurcahyati, Ica; Saragih, Triando Hamonangan; Farmadi, Andi; Kartini, Dwi; Muliadi, Muliadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.457

Abstract

The lungs are a very important part of the human body, as they serve as a place for oxygen exchange. They have a very complex task and are susceptible to damage from the polluted air we breathe every day, which can lead to various diseases. Lung disease is a very common health problem that can be found in everyone, but there are still many people who do not pay attention to their lung health, making them vulnerable to lung disease. One of the methods used to detect lung disorders is by examining images obtained from X-rays. Image processing is one of the techniques that can also be used for lung disease identification and is most commonly used in medical images. Therefore, the purpose of this research is to implement image processing to determine the accuracy of lung disease identification using deep learning algorithms and the application of feature extraction. In this research, there are two experiments conducted consisting of the application of the classification method, namely Convolutional Neural Network and Gray Level Co-Occurrence Matrix feature extraction with CNN. The results show that the CNN model gets a precision of 0.92, recall of 0.92, f1-score of 0.92, and average accuracy of 0.92. The combination of the GLCM method with CNN produces a precision of 0.87, recall of 0.87, f1-score of 0.87, and average accuracy of 0.87. The results of this study indicate that the use of CNN in the lung disease classification model based on X-ray images is superior to the GLCM-CNN method.
1D and 2D Feature Extraction Based on AAC and DC Protein Descriptors for Classification of Acetylation in Lysine Proteins using Convolutional Neural Network Faisal, Mohammad Reza; Adawiyah, Laila; Saragih, Triando Hamonangan; kartini, Dwi; Herteno, Rudy; Lumbanraja, Favorisen Rosyking; Handayani, Lilies; Solechah, Siti Aisyah
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.458

Abstract

Post-Translational Modification (PTM) denotes a biochemical alteration observed in an amino acid, playing crucial roles in protein activity, functionality, and the regulation of protein structure. The recognition of associated PTMs serves as a fundamental basis for understanding biological processes, therapeutic interventions for diseases, and the development of pharmaceutical agents. Using computational approaches (in silico) offers an efficient and cost-effective means to identify PTM sites swiftly. The exploration of protein classification commences with extracting protein sequence features that are subsequently transformed into numerical features for utilization in classification algorithms. Feature extraction methodologies involve using protein descriptors like Amino Acid Composition (AAC) and Dipeptide Composition (DC). Yet, these approaches exhibit a limitation by neglecting crucial amino acid sequence details. Moreover, both descriptor techniques generate a limited number of 1-dimensional (1D) features, which may not be ideal for processing through the Convolutional Neural Network (CNN) classification method. This investigation presents a novel approach to enhance feature diversity through protein sequence segmentation techniques, employing adjacent and overlapping segment strategies. Furthermore, the study illustrates the organization of features into 1D and 2D formats to facilitate processing through 1D CNN and 2D CNN classification methodologies. The findings of this research endeavour highlight the potential for enhancing the accuracy of acetylation classification in lysine proteins through the multiplication of protein sequence segments in a 2D configuration. The highest accuracy achieved for AAC and DC-based feature extraction methods is 77.39% and 76.75%, respectively.
Baby Cry Sound Detection: A Comparison of Mel Spectrogram Image on Convolutional Neural Network Models Junaidi, Ridha Fahmi; Faisal, Mohammad Reza; Farmadi, Andi; Herteno, Rudy; Nugrahadi, Dodon Turianto; Ngo, Luu Duc; Abapihi, Bahriddin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.465

Abstract

Baby cries contain patterns that indicate their needs, such as pain, hunger, discomfort, colic, or fatigue. This study explores the use of Convolutional Neural Network (CNN) architectures for classifying baby cries using Mel Spectrogram images. The primary objective of this research is to compare the effectiveness of various CNN architectures such as VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152 in detecting baby needs based on their cries. The datasets used include the Donate-a-Cry Corpus and Dunstan Baby Language. The results show that AlexNet achieved the best performance with an accuracy of 84.78% on the Donate-a-Cry Corpus dataset and 72.73% on the Dunstan Baby Language dataset. Other models like ResNet-50 and LeNet-5 also demonstrated good performance although their computational efficiency varied, while VGG-16 and VGG-19 exhibited lower performance. This research provides significant contributions to the understanding and application of CNN models for baby cry classification. Practical implications include the development of baby cry detection applications that can assist parents and healthcare provide.
Optimization of Backward Elimination for Software Defect Prediction with Correlation Coefficient Filter Method Muhammad Noor; Radityo Adi Nugroho; Setyo Wahyu Saputro; Rudy Herteno; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.466

Abstract

Detecting software defects is a crucial step for software development not only to reduce cost and save time, but also to mitigate more costly losses. Backward Elimination is one method for detecting software defects. Notably Backward Elimination may remove features that may later become significant to the outcome affecting the performance of Backward Elimination. The aim of this study is to improve Backward Elimination performance. In this study, several features were selected based on their correlation coefficient, with the selected feature applied to improve Backward Elimination final model performance. The final model was validated using cross validation with Naïve Bayes as the classification method on the NASA MDP dataset to determine the accuracy and Area Under the Curve (AUC) of the final model. Using top 10 correlation feature and Backward Elimination achieve an average result of 86.6% accuracy and 0.797 AUC, while using top 20 correlation feature and Backward Elimination achieved an average result of 84% accuracy and 0.812 AUC. Compare to using Backward Elimination and Naïve Bayes respectively the improvement using top 10 correlation feature as follows: AUC:1.52%, 13.53% and Accuracy: 13%, 12.4% while the improvement using top 20 correlation feature as follows: AUC:3.43%, 15.66% and Accuracy: 10.4%, 9.8%. Results showed that selecting the top 10 and top 20 feature based on its correlation before using Backward Elimination have better result than only using Backward Elimination. This result shows that combining Backward Elimination with correlation coefficient feature selection does improve Backward Elimination’s final model and yielding good results for detecting software defects.
The Comparison of Extreme Machine Learning and Hidden Markov Model Algorithm in Predicting The Recurrence Of Differentiated Thyroid Cancer Using SMOTE Aida, Nor; Saragih, Triando Hamonangan; Kartini, Dwi; Nugroho, Radityo Adi; Nugrahadi, Dodon Turianto
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.467

Abstract

Differentiated thyroid cancer is the most common type of thyroid cancer; the types in this category are papillary, follicular, and hurthel cell carcinoma. Up to 20% of DTCs will experience recurrence, although this figure reduces to 5% in low-risk patients. There is still little research on thyroid cancer prediction using a machine learning approach, especially the prediction recurrence of DTCs. This research aims to compare the performance of the Extreme Learning Machine and the Hidden Markov Model using SMOTE in predicting the recurrence of DTCs. The dataset used in this research is differentiated thyroid cancer recurrence from Kaggle. This research methodology comprises preprocessing, data sharing, SMOTE, ELM and HMM modeling algorithms, and evaluation. ELM with SMOTE gets the best results at a ratio of 90:10 with 35 hidden neurons that get an accuracy value of 1.00, precision 1.00, recall 1.00, and AUC 1.00. ELM modeling gets the best results at a ratio of 90:10 with 45 hidden neurons that get an accuracy value of 1.00, precision 1.00, recall 1.00, and AUC 1.00. HMM modeling gets the best value at a ratio of 70:30 with two hidden states and two iterations, which get an accuracy value of 0.8696, precision 0.8696, recall 0.7944, and AUC 0.9575. Last, HMM modeling with SMOTE gets the best results at a ratio of 60:40 with two hidden states and two iterations, with an accuracy value of 0.8696, precision of 0.8832, recall of 0.7848, and AUC of 0.9174. Based on the results of this study, it can be concluded that ELM with SMOTE gets the best performance, followed by ELM without SMOTE, HMM without SMOTE, and finally, HMM with SMOTE. The implication is that ELM with SMOTE can produce high accuracy in predicting the recurrence of DTCs.
Unlocking Early Detection and Intervention Potential: Analyzing Visual Evoked Potentials (VEPs) in Adolescents/Teenagers with Narcotics Abuse Tendencies from the TelUnisba Neuropsychology EEG Dataset (TUNDA) Wijayanto, Inung; Sulistyo, Tobias Mikha; Nur Pratama, Yohanes Juan; Safitri, Ayu Sekar; Rahmaniar, Thalita Dewi; Sa’idah, Sofia; Hadiyoso, Sugondo; Wibowo, Raiyan Adi; Kurnia Ismanto, Rima Ananda; Putri, Athaliqa Ananda; Khasanah, Andhita Nurul; Diliana, Faizza Haya; Azzahra, Salwa; Gadama, Melsan; Utami, Ayu Tuty
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.476

Abstract

Narcotics abuse has extensive negative impacts on individuals, families, and society, including physical harm to organs and mental health disorders. Addressing teenage narcotics problems requires collaborative efforts involving educational institutions, families, and psychologists. Currently, narcotics has increasingly targeted teenagers, becoming a serious issue that demands special attention in prevention and treatment. Handling narcotic problems at the adolescent level necessitates close collaboration among educational institutions, families, and the community, including psychologists. Emphasizing the importance of early detection and prevention, this study proposes a method to detect the possibility of narcotic abuse in adolescents using the Go/No-Go Association Task (GNAT) test designed by psychologists. The study introduced the TelUnisba Neuropsychology EEG Dataset (TUNDA), an open EEG dataset with data on the emotional and habitual aspects of drug abuse in Indonesia, classified into "normal" and "risk" by psychologists. The processed EEG signal is the visual evoked potential (VEP) within 1000 milliseconds following the visual stimulus onset. The data is classified as “slow” and “fast” based on respondent's responses using MobileNetV2 architecture. Results showed MobileNetV2 achieved the highest accuracy for both normal and risk categories, with accuracies of 0.86 and 0.85 respectively. This study obtained ethical clearance and received funding support from Telkom University and Universitas Islam Bandung, with technical assistance from the Smart Data Sensing Laboratory. The authors declare no conflicts of interest related to this study.

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