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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 199 Documents
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Enhancing Software Defect Prediction: HHO-Based Wrapper Feature Selection with Ensemble Methods Fauzan Luthfi, Achmad; Herteno, Rudy; Abadi, Friska; Adi Nugroho, Radityo; Itqan Mazdadi, Muhammad; Athavale, Vijay Anant
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/f2140043

Abstract

The growing complexity of data across domains highlights the need for effective classification models capable of addressing issues such as class imbalance and feature redundancy. The NASA MDP dataset poses such challenges due to its diverse characteristics and highly imbalanced classes, which can significantly affect model accuracy. This study proposes a robust classification framework integrating advanced preprocessing, optimization-based feature selection, and ensemble learning techniques to enhance predictive performance. The preprocessing phase involved z-score standardization and robust scaling to normalize data while reducing the impact of outliers. To address class imbalance, the ADASYN technique was employed. Feature selection was performed using Binary Harris Hawk Optimization (BHHO), with K-Nearest Neighbor (KNN) used as an evaluator to determine the most relevant features. Classification models including Random Forest (RF), Support Vector Machine (SVM), and Stacking were evaluated using performance metrics such as accuracy, AUC, precision, recall, and F1-measure. Experimental results indicated that the Stacking model achieved superior performance in several datasets, with the MC1 dataset yielding an accuracy of 0.998 and an AUC of 1.000. However, statistical significance testing revealed that not all observed improvements were meaningful; for example, Stacking significantly outperformed SVM but did not show a significant difference when compared to RF in terms of AUC. This underlines the importance of aligning model choice with dataset characteristics. In conclusion, the integration of advanced preprocessing and metaheuristic optimization contributes positively to software defect prediction. Future research should consider more diverse datasets, alternative optimization techniques, and explainable AI to further enhance model reliability and interpretability.
Analysis of the Effect of Feature Extraction on Sentiment Analysis using BiLSTM: Monkeypox Case Study on X/Twitter Noryasminda; Saragih, Triando Hamonangan; Herteno, Rudy; Faisal, Mohammad Reza; Farmadi, Andi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.73

Abstract

The monkeypox outbreak has again become a global concern due to its widespread spread in various countries. Information related to the disease is widely shared through social media, especially Twitter which is a major source of public opinion. However, the complexity of language and the diverse viewpoints of users often pose challenges in accurately analyzing sentiment. Therefore, sentiment analysis of tweets about monkeypox is important to understand public perception and its impact on the dissemination of health information. This research contributes to identifying the most effective word embedding-based feature extraction method for sentiment analysis of health issues on social media. The purpose of this study is to compare the performance of word embedding methods namely Word2Vec, GloVe, and FastText in sentiment analysis of tweets about monkeypox using the BiLSTM model. Data totaling 1511 tweets were collected through a crawling process using the Twitter API. After the data is collected, manual labeling is done into three sentiment categories, namely positive, negative, and neutral. Furthermore, the data is processed through a preprocessing stage which includes data cleaning, case folding, tokenization, stopword removal, and stemming. The evaluation results show that FastText with BiLSTM produces the highest accuracy of 90%, followed by Word2Vec at 89%, and GloVe at 87%. FastText proved to be more effective in reducing classification errors, especially in distinguishing between negative and positive sentiments due to its ability to capture subword information and broader context. These findings suggest that the use of FastText can improve the accuracy of sentiment analysis, especially on health issues that develop on social media, so that it can support data-driven decision making by relevant parties in handling information dissemination. 
The Role of U-Net Segmentation for Enhancing Deep Learning-based Dental Caries Classification Yassar, Muhammad Keysha Al; Fitria, Maya; Oktiana, Maulisa; Yufnanda, Muhammad Aditya; Saddami, Khairun; Muchtar, Kahlil; Isma, Teuku Reza Auliandra
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.75

Abstract

Dental caries, one of the most prevalent oral diseases, can lead to severe complications if left untreated. Early detection is crucial for effective intervention, reducing treatment costs, and preventing further deterioration. Recent advancements in deep learning have enabled automated caries detection based on clinical images; however, most existing approaches rely on raw or minimally processed images, which may include irrelevant structures and noise, such as the tongue, lips, and gums, potentially affecting diagnostic accuracy. This research introduces a U-Net-based tooth segmentation model, which is applied to enhance the performance of dental caries classification using ResNet-50, InceptionV3, and ResNeXt-50 architectures. The methodology involves training the teeth segmentation model using transfer learning from backbone architectures ResNet-50, VGG19, and InceptionV3, and evaluating its performance using IoU and Dice Score. Subsequently, the classification model is trained separately with and without segmentation using the same hyperparameters for each model with transfer learning, and their performance is compared using a confusion matrix and confidence interval. Additionally, Grad-CAM visualization was performed to analyze the model's attention and decision-making process. Experimental results show a consistent performance improvement across all models with the application of segmentation. ResNeXt-50 achieved the highest accuracy on segmented data, reaching 79.17%, outperforming ResNet-50 and InceptionV3. Grad-CAM visualization further confirms that segmentation plays a crucial role in directing the model’s focus to relevant tooth areas, improving classification accuracy and reliability by reducing background noise. These findings highlight the significance of incorporating tooth segmentation into deep learning models for caries detection, offering a more precise and reliable diagnostic tool. However, the confidence interval analysis indicates that despite consistent improvements across all metrics, the observed differences may not be statistically significant.
Evaluation of the Impact of SMOTEENN on Monkeypox Case Classification Performance Using Boosting Algorithms Siena, Laifansan; Saragih, Triando Hamonangan; Nugroho, Radityo Adi; Kartini, Dwi; Muliadi; Caesarendra, Wahyu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.77

Abstract

Monkeypox is a zoonotic disease with increasing global prevalence, posing a significant challenge in healthcare. Its widespread transmission necessitates more accurate detection systems to assist medical professionals in diagnosing and managing cases effectively. One of the main challenges in developing monkeypox prediction models is class imbalance in datasets, which can cause models to favor the majority class and reduce predictive accuracy for rarer cases. To address this issue, this study evaluates the effectiveness of the SMOTEENN resampling technique in improving the classification performance of monkeypox cases. Three boosting algorithms Gradient Boosting, XGBoost, and LightGBM were applied to a monkeypox dataset consisting of 25,000 samples. The data preprocessing steps included handling missing values, feature encoding, and feature scaling. The dataset was then balanced using SMOTEENN, a hybrid technique combining the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN). Additionally, hyperparameter tuning with GridSearchCV was performed to optimize model performance by systematically selecting the best parameter combinations. The results indicate that applying SMOTEENN significantly improved classification accuracy, achieving a maximum of 69%, with an F1-score of 67%. Compared to previous studies, the proposed approach demonstrated superior performance in handling class imbalance and enhancing classification robustness. These findings highlight the potential of SMOTEENN and boosting algorithms in medical data classification, particularly for infectious diseases with imbalanced datasets. This study contributes to the development of more reliable machine learning techniques for improving disease detection, classification accuracy, and overall model generalization. Future research should explore additional resampling techniques, deep learning architectures, and feature selection methods to further improve predictive performance in medical diagnostics.
An Explainable Artificial Intelligence Framework for Breast Cancer Detection Ridha, Jamalur; Saddami, Khairun; Riswan, Muhammad; Roslidar, Roslidar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.78

Abstract

Breast cancer remains a leading cause of mortality among women worldwide, primarily due to delayed detection and a lack of early awareness. To address this issue, this study develops an advanced, thermal image-based breast cancer detection system that is non-invasive, radiation-free, and cost-effective, enhanced through the integration of artificial intelligence (AI) techniques. The proposed framework incorporates Attention U-Net for accurate segmentation of thermal breast images, K-Means Clustering to localize and isolate high-temperature regions suspected to be cancerous, and an EfficientNet-B7-based Convolutional Neural Network (CNN) for classification. To increase clinical reliability and transparency, the system employs Explainable AI (XAI) techniques using Local Interpretable Model-Agnostic Explanations (LIME), which provide visual interpretations of the model’s decision-making process. The dataset used in this research was obtained from the Database for Mastology Research (DMR) and consists of 2010 thermal images, including both healthy and abnormal cases. Preprocessing and segmentation effectively remove irrelevant areas and focus on the breast region, enhancing detection accuracy. Experimental evaluation indicates the proposed model achieves a training accuracy of 96.48% and a validation accuracy of 91.67%, with a recall of 91.95%, specificity of 91.43%, precision of 89.89%, and F1-score of 90.91%. These results highlight the system’s robust performance and generalizability. The LIME-generated superpixel visualizations help medical professionals better understand and validate the model's predictions, contributing to increased trust in AI-driven diagnostics. Overall, this research presents a reliable, explainable, and ethically grounded solution for early-stage breast cancer detection, demonstrating its strong potential for supporting clinical decision-making and future deployment in real-world healthcare settings.
Efficiency Of Transition From Manual Medical Records To Electronic Medical Records On  The Speed Of Patient Service At RSU Royal Prima Medan Laurenxius, Vanessa Sinana; Ginting, Chrismis Novalinda; Ginting, Rapael
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.79

Abstract

In the digital era, the integration of quality and routine data is key to transforming healthcare services. The utilization of information technology continues to grow, spanning from planning to providing individual and community health data. One of the innovations implemented is the transition from manual medical records to electronic medical records (RME), which despite its many benefits, still faces various challenges. RSU Royal Prima Medan started this transition process at the end of 2023 until early 2024, with the main obstacles being infrastructure limitations, suboptimal system integration, and adaptation difficulties that impacted the efficiency of patient services.  This study aims to analyze the efficiency of the RME transition in improving patient service speed at RSU Royal Prima Medan. The method used was descriptive qualitative with an exploratory approach, involving in-depth interviews, direct observation, and document analysis. Data were analyzed using the Miles and Huberman interactive model. The results showed that the implementation of RME has achieved about 80% effectiveness in improving service efficiency. The data retrieval process became 50% faster than the manual system, while the RME utilization rate reached 85%. Patient waiting time was also reduced by an average of 15 minutes per consultation. Despite this, obstacles such as network instability and technical issues were still encountered. In conclusion, the transition to RME at RSU Royal Prima Medan has improved service efficiency and patient data management. However, continuous system development, more comprehensive integration, as well as infrastructure improvements are needed to optimize the benefits of RME in improving healthcare quality
Machine Learning Implementation for Sentiment Analysis on X/Twitter: Case Study of Class Of Champions Event in Indonesia Hafizah, Rini; Saragih, Triando Hamonangan; Muliadi, Muliadi; Indriani, Fatma; Mazdadi, Muhammad Itqan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.81

Abstract

Sentiment analysis on social media is becoming an important approach in understanding public opinion towards an event. Twitter, as a microblogging platform, generates a large amount of data that can be utilized for this analysis. This study aims to evaluate and compare the performance of three classification algorithms, namely Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost), in sentiment analysis related to the Clash of Champions event in Indonesia. To represent the text data, two feature extraction techniques are used, namely Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW). In addition, Synthetic Minority Over-sampling Technique (SMOTE) is applied to handle data imbalance, while model optimization is performed using GridSearchCV. The research dataset consists of 1,000 tweets collected through web scraping, then manually processed and labeled before model training and testing. The results showed that the TF-IDF technique provided superior results compared to BoW. The Random Forest model with TF-IDF achieved the highest accuracy of 91%, while XGBoost with TF-IDF had the highest Area Under the Curve (AUC) of 0.91. The findings confirm that the selection of appropriate feature extraction techniques and algorithms can improve accuracy in sentiment analysis. This study can be applied in public opinion monitoring and data-driven decision-making. Future research can explore word embedding techniques and transformer-based deep learning models to improve semantic understanding and accuracy of sentiment analysis.
Implementation of Bayesian Structural Time Series (BSTS) Method for Predicting Traditional Market Revenue Achievement in Surabaya Muizzadin, Muizzadin; Mohammad Idhom; Damaliana, Aviolla Terza
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.82

Abstract

Traditional markets play an important role in the regional economy, including in the city of Surabaya. However, the number of traditional markets in Surabaya has continued to decline in recent years due to competition with modern markets. In addition, the contribution of traditional markets to Regional Original Income (PAD) has fluctuated, for example 1.67% in 2013, 1.66% in 2014, and increased to 1.76% in 2015. This condition poses a challenge for the management of regional economic policies, so an accurate prediction method is needed to support strategic decision making. This study aims to predict the achievement of traditional market revenue in Surabaya using the Bayesian Structural Time Series (BSTS) method. The data used is the percentage of traditional market revenue achievement over the past fifteen years. The BSTS model is applied with various components, including Local Level, Local Linear Trend, and Seasonal, which allows flexibility in capturing trends, seasonal patterns, and structural changes in the data. Model evaluation is carried out using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to assess prediction accuracy. The results of the study showed that the BSTS model with Local Level and Seasonal components and 1,000 MCMC iterations provided the best performance, with a MAPE value of 4.036% and an RMSE of 5.198. This model is able to capture trend and seasonal patterns well, making it effective in predicting traditional market revenue achievements. Based on these findings, the BSTS method has proven to be a reliable approach in predicting traditional market revenue achievements. The results of this study are expected to help market managers and policy makers in designing more adaptive strategies to maintain the competitiveness of traditional markets and increase their contribution to the regional economy.
Combinations of Optimization Method and Balancing Technique in Hypertension Classification with Machine Learning Lu'o, Natalia Intan Suryani; Sengkey, Daniel Febrian; Joseph, Victor Florencia Ferdinand
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.86

Abstract

Hypertension is a condition in which blood vessels experience continuous pressure higher than normal limits which can cause pain and even death. Hypertension is classified into several classes based on the measured blood pressure. To correctly diagnose hypertension is a critical task that requires medical specialists who are unfortunately not evenly distributed in every region. This research aims to implement Particle Swarm Optimization for hyperparameter tuning in machine learning algorithms in hypertension disease classification. The approach was developed by comparing the performance of Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extra Trees (ET). Each algorithm was trained using its default hyperparameters, tuned with Grid Search and Cross-validation (GSCV), and the Particle Swarm Optimization with Cross-validation (PSO-CV). We consider recall to be the primary evaluation metric due to the imbalance in the dataset. The experiment results show that the combination of the LGBM and PSO-CV is the best combination of algorithm and hyperparameter optimization method with precision, recall, F1-score, ROC-AUC, and PR-AUC values of 0.22, 0.63, 0.33, 0.79, and 0.24, respectively. The results of this study prove that PSO might positively influence model performance, particularly in the case of unbalanced data.
Multi-Channel Trans-Admittance Imaging for Anomaly Detection Aprihapsari, Fransiska Maria; Octavia, Atin Asna; Ain, Khushnul; Ariwanto, Bayu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.88

Abstract

This research aims to develop an Electrical Impedance Tomography (EIT) system to detect anomalies in the network. The developed system utilizes 64 copper-based electrodes and Analog Discovery 2 as a real-time sinusoidal current source. The frequencies used are 50, 80, 100, 120, and 150 kHz because these frequency ranges are able to capture differences in network conductivity values. Each time data is captured, the system generates three main values, namely real, imaginary, and magnitude impedance values, each of which represents the conductivity conditions in the measurement area at 64 electrode points. The values are then converted into RGB format and mapped to be visualized in the form of an 8-bit digital image. In the reconstructed image, areas with significant differences in conductivity will appear with increasingly brownish shades of color, indicating a potential anomaly. The anomaly detection method in this study uses a 0.9% NaCl saline solution as the main medium. As a model of tissue anomalies, natural materials such as jicama and carrots are used. The anomaly position is varied at distances of 4 cm, 5 cm, 6 cm, and 7 cm, which is adjusted to the dimensions of the available acrylic containers to ensure systematic evaluation of the system against detection sensitivity at various depths. The results showed that the system was able to detect the difference in conductivity values between the anomaly and the anomalous area. Higher conductivity values are consistently measured in areas where anomalies are present compared to areas where there are no anomalies. The most optimal detection occurs at a measurement distance of 4 cm, as well as at the lowest frequency, which is 50 kHz.This method is an innovative solution for non-invasive and safe detection of tissue anomalies, without exposure to ionizing radiation, making it suitable for early diagnostic procedures in various medical fields, such as breast cancer detection, tumor identification, and other diagnostic applications. In addition, the system is low-cost and easy to use because the result is a visual image that makes it easy for medical personnel to interpret and analyze.

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