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INDONESIA
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568624     DOI : https://doi.org/10.35882/ijeeemi
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) publishes peer-reviewed, original research and review articles in an open-access format. Accepted articles span the full extent of the Electronics, Biomedical, and Medical Informatics. IJEEEMI seeks to be the world’s premier open-access outlet for academic research. As such, unlike traditional journals, IJEEEMI does not limit content due to page budgets or thematic significance. Rather, IJEEEMI evaluates the scientific and research methods of each article for validity and accepts articles solely on the basis of the research. Likewise, by not restricting papers to a narrow discipline, IJEEEMI facilitates the discovery of the connections between papers, whether within or between disciplines. The scope of the IJEEEMI, covers: Electronics: Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, Electromedical: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design. Medical Informatics: Intelligent Biomedical Informatics, Computer-aided medical decision support systems using heuristic, Educational computer-based programs pertaining to medical informatics
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 17 Documents
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Search results for , issue "Vol. 7 No. 1 (2025): February" : 17 Documents clear
Application of Heuristic Algorithm in Medical Informatics System for Patient Data Management in Southeast Asia Setyani, Seswiyati Asri; Mahendro Prasetyo Kusumo
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/7ygpx615

Abstract

This research aims to analyze the application of heuristic algorithms in medical informatics systems for managing patient data in Southeast Asia. This research method uses qualitative research. This research data uses secondary data. Data includes scientific literature, technical reports, policy documents, and statistical data from trusted sources such as reputable journals, conferences, books, and health institutional reports. Data analysis using Nvivo 12 Plus software to identify relevant patterns and themes. This research indicates that Southeast Asian countries have begun to adopt information technology in health services. The implementation of the Electronic Medical Record (EMR) System has been used in several large hospitals in Malaysia and Thailand to integrate patient data digitally. The most prominent theme in the data findings regarding the role of heuristic algorithms in managing patient data is the importance of data security, management, optimization, efficiency, and blockchain integration in patient health services. Plus, the challenges heuristic algorithms face in healthcare include infrastructure, data fragmentation, cross-platform system integration, data security and privacy, big data analysis (big data), and potential heuristic algorithms. The significant impact is improving the efficiency, quality, and accessibility of health services. One of the main impacts is increasing operational efficiency. The implications of this research recommend that policymakers and health institutions adopt heuristic algorithms in medical informatics systems for managing patient data in hospitals to improve the quality of health services
Implementation of Copeland Method on Wrapper-Based Feature Selection Using Random Forest For Software Defect Prediction Aryanti, Agustia Kuspita; Herteno, Rudy; Indriani, Fatma; Nugroho, Radityo Adi; Muliadi, Muliadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/2pgffc67

Abstract

Software Defect Prediction is crucial to ensure software quality. However, high-dimensional data presents significant challenges in predictive modelling, especially identifying the most relevant features to improve model performance. Therefore, efforts are needed to address these issues, and one is to apply feature selection methods. This study introduces a new approach by applying the Copeland ranking method, which aggregates feature weights from multi-wrapper methods, including Recursive Feature Elimination (RFE), Boruta, and Custom Grid Search, using 12 NASA MDP datasets. The study also applies Random Forest classification and evaluates the model using AUC and t-Test. In addition, this study also compares the accuracy and precision values produced by each method. The results consistently show that the Copeland ranking method produces superior results compared to other ranking methods. The average AUC value obtained from the Copeland ranking method is 0.7496, higher than the Majority ranking method with an average AUC of 0.7416 and the Optimal Rank ranking method with an average AUC of 0.7343. These findings confirm that applying the Copeland ranking method in wrapper-based feature selection can enhance classification performance in software defect prediction using Random Forest compared to other ranking methods. The strength of the Copeland method lies in its ability to integrate rankings from various feature selection approaches and identify relevant features. The findings of this research demonstrate the potential of the Copeland ranking method as a reliable tool for ranking features obtained from various wrapper-based feature selection techniques. The implementation of this approach contributes to improved software defect prediction and provides new insights for the development of ranking methods in the future
Dimensionality Reduction Using Principal Component Analysis and Feature Selection Using Genetic Algorithm with Support Vector Machine for Microarray Data Classification Kartini, Dwi; Badali, Rahmat Amin; Muliadi, Muliadi; Nugrahadi, Dodon Turianto; Indriani, Fatma; Saputro, Setyo Wahyu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/mr7x9713

Abstract

DNA microarray is used to analyze gene expression on a large scale simultaneously and plays a critical role in cancer detection. The creation of a DNA microarray starts with RNA isolation from the sample, which is then converted into cDNA and scanned to generate gene expression data. However, the data generated through this process is highly dimensional, which can affect the performance of predictive models for cancer detection. Therefore, dimensionality reduction is required to reduce data complexity. This study aims to analyze the impact of applying Principal Component Analysis (PCA) for dimensionality reduction, Genetic Algorithm (GA) for feature selection, and their combination on microarray data classification using Support Vector Machine (SVM). The datasets used are microarray datasets, including breast cancer, ovarian cancer, and leukemia. The research methodology involves preprocessing, PCA for dimensionality reduction, GA for feature selection, data splitting, SVM classification, and evaluation. Based on the results, the application of PCA dimensionality reduction combined with GA feature selection and SVM classification achieved the best performance compared to other classifications. For the breast cancer dataset, the highest accuracy was 73.33%, recall 0.74, precision 0.75, and F1 score 0.73. For the ovarian cancer dataset, the highest accuracy was 98.68%, recall 0.98, precision 0.99, and F1 score 0.99. For the leukemia dataset, the highest accuracy was 95.45%, recall 0.94, precision 0.97, and F1 score 0.95. It can be concluded that combining PCA for dimensionality reduction with GA for feature selection in microarray classification can simplify the data and improve the accuracy of the SVM classification model. The implications of this study emphasize the effectiveness of applying PCA and GA methods in enhancing the classification performance of microarray data.
Optimizing Clustering Analysis to Identify High-Potential Markets for Indonesian Tuber Exports Prasetya, Dwi Arman; Sari, Anggraini Puspita; Idhom, Mohammad; Lisanthoni, Angela
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/skzqbd57

Abstract

Agriculture is a key contributor to Indonesia's economic growth, with tubers representing the second most important food crop. Despite their significance, the export value of Indonesia’s tuber crops has not yet reached its full potential given the decline in the value of tuber exports since 2021. One of the contributing factors is the restricted range of export market options. This study aims to analyze export trade patterns to identify the most high-potential markets for Indonesian tuber commodities.  Clustering analysis is used as a key method to identify market locations by grouping countries based on similar trade characteristics. Clustering was conducted using the Gaussian Mixture Model (GMM), which enhanced by Particle Swarm Optimization (PSO) and evaluated by silhouette score and DBI. The dataset is collected from Indonesia’s Central Bureau of Statistics from 2019 to 2023, focusing on 5 kinds of tuber exports with total of 455 entries and 8 columns. Using the AIC/BIC method, the optimal number of clusters obtained is 2 which are low market opportunities (cluster 0) and high market oppurtunities (cluster 1). Results showed that the GMM model without optimization has silhouette score of 0.7602 and DBI of 0.8398, while the GMM+PSO model achieved an improved silhouette score of 0.8884 and DBI of 0.5584. Both score are categorized as strong structure but, GMM+PSO has higher silhouette score and lower DBI score, demonstrating the effectiveness of PSO in enhancing the clustering model’s performance. The key potential markets for Indonesian tuber exports are primarily concentrated in Asia, including countries such as China, Malaysia, Thailand, Vietnam, Hong Kong, and United States.
Implementation of Vision Transformer for Early Detection of Autism Based on EEG Signal Heatmap Visualization Rafiki, Aufa; Melinda, Melinda; Oktiana, Maulisa; Dewi Meutia, Ernita; Afnan, Afnan; Mulyadi, Mulyadi; Zakaria, Lailatul Qadri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/40n05b64

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behavioral patterns. Early detection of ASD is crucial for improving the quality of life of affected individuals and alleviating the burden on their families. This study proposes a computer-aided diagnostic system for ASD by applying a pre-trained Vision Transformer (ViT-B/16) architecture to EEG signal data obtained from King Abdul Aziz University. The dataset comprises EEG recordings from 16 subjects (8 normal and 8 ASD) that have undergone preprocessing—including filtering using the Discrete Wavelet Transform (DWT), segmentation (windowing), and conversion into heatmap representations—and were subsequently partitioned into training, validation, and testing subsets. The ViT model was trained for 100 epochs with a batch size of 16, using the AdamW optimizer and the CrossEntropy loss function, while two learning rate configurations (0.0001 and 0.00001) were evaluated; the best-performing weights were selected based on the lowest validation loss. Test results indicate that the model trained with a learning rate of 0.00001 achieved a testing accuracy of 99.53%, accompanied by excellent precision, specificity, recall, and f1-score, thereby demonstrating strong generalization capabilities and minimal overfitting. Future research is recommended to incorporate locally sourced datasets and to further customize the ViT architecture through comprehensive hyperparameter tuning, with the aim of developing a mobile application to support clinical ASD diagnosis.
Expert System for Early Detection of Thalassemia Disease Using Case-Based Reasoning Method Setiani, Rahma; Djatmiko, Wahyu; Kurniawan, Rozali Arsyad; Abdullayev, Vugar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/gt9h2k22

Abstract

Thalassemia is a blood disorder characterized by abnormalities in globin chain formation. In Banyumas Regency, the prevalence of thalassemia continues to increase yearly, while detection processes are often delayed due to limited access to experts. This study aims to develop a web-based expert system for the early detection of thalassemia using the Case-Based Reasoning (CBR) method with the K-Nearest Neighbor (KNN) algorithm. The system is designed to help identify individuals who may carry the thalassemia gene trait, enabling faster and more accurate treatment. The system was tested using the black box method to ensure all features function properly across all user roles, including general users, administrators, and experts. Accuracy evaluation was conducted using a confusion matrix, achieving an accuracy rate of 95,23% based on 21 test data samples. The results indicate that this system provides highly accurate early detection and supports preventive efforts against thalassemia. Further development is recommended to create an Android-based application to enhance accessibility for the broader community. Additionally, continuous updates to the knowledge base are necessary to improve the system's accuracy and scope. This study is expected to contribute to the prevention and management of thalassemia, increase public awareness, and support better healthcare services in Indonesia
Bioelectrical Impedance Spectroscopy (BIS) For Ratiometric Identification Alhaq, Elmira Rofida; Salwa, Umaimah Mitssalia Umi; Ain, Khusnul; Sapuan, Imam
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/j81sf349

Abstract

This research explores the potential use of Electrical Impedance Spectroscopy (EIS) and ratiometric methods to improve security and reproducibility in bioelectrical impedance-based biometric authentication systems. Traditional biometric technologies such as fingerprints are susceptible to forgery and less effective in handling external variations, making bioelectric signal-based approaches a promising alternative. By using Analog Discovery 2 to measure the impedance of ten pairs of fingers in the frequency range of 20 kHz to 500 kHz, with a 1 mA sinusoidal current injected into the subject's fingers, real-time data collection can be performed with the precision required for biometric applications. The measurement results show that the impedance value for each finger differs among subjects, making it a useful parameter for biometric authentication. The application of the ratiometric method successfully reduces day-to-day measurement variations, especially at high frequencies above 100 kHz, resulting in more stable and consistent data. This research shows that bioelectrical impedance methods have the potential to improve security compared to traditional methods such as fingerprinting, as they are more difficult to replicate. This approach offers a promising solution for a more secure and highly reproducible biometric authentication system, with potential applications in various security systems and wearable technologies.

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