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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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Few-shot Classification of Smartphone Photos using Hidden Markov Model and Siamese Network Hatala, Zulkarnaen; Hudzaly, Muhammad
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.116

Abstract

Images from the increasing use of smartphones are so large that they are nearly impossible to handle by hand. The problem arises when a person needs to classify these photos into groups or classes. Smartphones are low-performance devices in contrast to desktop or cloud-based computers. Many solutions of image classification using various types of Convolutional Neural Network (CNN) are performed on massive cloud-based supercomputers. These computers often equipped with very high-end additional specialized graphics processing units (GPUs) at remarkable prices. In fact, to implement classification in most smartphones currently on the market, we need an algorithm that has less computation. Based on this fact, we propose HMM that requires fewer parameters. The aim of this research is to examine HMM method for classification of photos taken with a smartphone. For a comparison we also outline the results from Siamese CNN. The same data are used for training and testing for both models. For HMM, we use Discrete Cosine Transform (DCT) to extract salient features of images. The number of training examples is very small compared to the test set. Here we carried out few-shot classification method. In the training phase, we used Maximum Likelihood (ML) criterion-based, Baum-welch algorithm. Two versions are used; isolated training is applied first and later followed by jointly-embedded Baum-welch estimation of parameters. For recognition of the HMM, Viterbi algorithm is applied. Performances of both procedures were measured. Based on the test results, HMM achieves 0,94 precision, 0.85 recall, F1 score 0.89 and accuracy 0.90 while Siamese claims 0.87, 0.98, 0.92 and 0.91. The result shows that HMM, which has advantage over Siamese in term of fewer parameters number, still competes Siamese CNN with only slight decrease in performance. We conclude that HMM are suitable over Siamese CNN to be implemented in low-performance devices such as cellphones.
Improving Diabetes Prediction Using Feedforward Neural Network with Adam Optimization and SMOTE Technique Wijaya Kusuma, Arizha; Mazdadi, Muhammad Itqan; Kartini, Dwi; Farmadi, Andi; Indriani, Fatma; P., Chandrasekaran
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.127

Abstract

Diabetes mellitus is a chronic metabolic disorder that demands early and accurate detection to prevent life-threatening complications. Traditional diagnostic procedures, such as blood glucose tests and oral glucose tolerance tests, are often invasive, time-consuming, and resource-intensive, making them less practical for widespread screening. This study aims to explore the potential of artificial intelligence, specifically Feedforward Neural Networks (FNN), in predicting diabetes based on clinical data from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The main contribution of this research lies in the application of the Adaptive Moment Estimation (Adam) optimization algorithm and the Synthetic Minority Oversampling Technique (SMOTE) to enhance the performance and generalization of the FNN on imbalanced medical datasets. The methodology involves preprocessing steps such as imputing zero values with feature means, normalizing input features using Min-Max scaling, and applying SMOTE to balance class distribution. Two model configurations were compared: a baseline FNN trained manually using full-batch gradient descent and a second FNN optimized using Adam. Experimental results demonstrated that the baseline model achieved an accuracy of 70.13%, precision of 56.06%, recall of 68.52%, and F1-score of 61.67%, while the Adam-optimized model achieved superior results with an average accuracy of 73.31%, precision of 60.97%, recall of 66.67%, and F1-score of 63.64% across ten independent runs. These findings indicate that combining adaptive optimization with oversampling significantly enhances the robustness and reliability of neural networks for medical classification tasks. In conclusion, the proposed method provides an effective framework for AI-assisted early diabetes detection and opens pathways for future development using deeper network architectures and explainable AI models for clinical applications.
Automated Z-Score Based Nutritional Status Classification for Children Under Two Using Smart Sensor System Yunidar, Yunidar; Melinda, Melinda; Ridara, Rina; Basir, Nurlida
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.111

Abstract

The classification of nutritional status in children under two years old is crucial for monitoring growth and early detection of nutritional problems. However, in many healthcare facilities, this classification is still performed manually, requiring relatively long processing times and being prone to human error in both measurement and data recording. The problem addressed in this study is the inefficiency and potential inaccuracy of manual nutritional status classification in toddlers. This research aims to develop an automatic and digital device capable of measuring body length and weight and classifying nutritional status in children under two years old efficiently, accurately, and in real time. The device utilizes electronic sensors integrated with a microcontroller to streamline the process and reduce measurement error. The main contribution of this study is the design and realization of a portable automation device that integrates an HC-SR04 ultrasonic sensor for measuring body length and a 50 kg full-bridge load cell sensor for measuring body weight, both controlled by an ATmega328P microcontroller. The device processes the data measurement digitally, displays the results on a 20 × 4 LCD, and provides a printed copy via a thermal printer, enhancing the data recording efficiency. The method involves the design of hardware circuits, sensor calibration, software programming using the C language in the Arduino IDE, and performance testing of the device by comparing its results to standard measuring instruments. The device’s performance is evaluated based on measurement error percentage and precision level. The results demonstrate that the device achieved an error percentage of 1.26% for body length measurement and 0.98% for body weight measurement. The overall system error is recorded at 0.5%, with a precision level ranging from ±0.08 to ±0.4.
Digital Innovations in Patient-Centered Care: The Emerging Role of Natural Language Processing Putra, Bima Ananta; Arini, Merita; Tamaranny, Yoviena Kusuma Terichtia
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.115

Abstract

Patient-Centered Care (PCC) faces critical challenges such as fragmented communication, limited interpretation of patient narratives, and underutilization of real-time feedback. Natural Language Processing (NLP) offers promising solutions by enabling the structured analysis of unstructured data like Electronic Health Records (EHRs), social media content, and patient feedback. This study aims to systematically map the scholarly landscape of NLP applications in PCC between 2015 and 2025, identifying key trends, dominant research themes, and knowledge gaps. A bibliometric analysis was conducted using the Scopus database, with inclusion criteria focused on peer-reviewed, English-language articles in relevant health and technology fields. From an initial set of 645 records, 254 publications met the eligibility requirements. Data cleaning and network analysis were performed using OpenRefine, MS Excel, and VOSviewer, focusing on co-authorship, keyword co-occurrence, and citation density. Results indicate an exponential increase in research output, rising from five publications in 2015 to eighty-one in 2024, largely driven by high-income countries with advanced digital infrastructure. Five thematic clusters emerged: (1) Social Media–Based Patient Communication, (2) Sentiment Analysis for Care Feedback, (3) Clinical Decision Support via NLP, (4) AI-Powered Patient Empowerment, and (5) Modeling Perceived Quality of Care. Implications include the development of real-time, AI-driven feedback loops, multimodal data integration, and culturally responsive chatbot systems. This study also highlights urgent directions for future research, such as building explainable and ethical AI models, integrating diverse data sources, and designing adaptive NLP applications that support longitudinal patient engagement. It offers foundational insights into the evolving role of NLP in enhancing personalized, responsive, and ethically sound PCC.
Analysis of the Application of Machine Learning Algorithms for Classification of Toddler Nutritional Status Based on Anthropometric Data Yamasari, Yuni; Yogiyanti, Esti; Yohannes, Ervin
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.110

Abstract

The rapid advancement of technology has required appropriate strategies to achieve accurate and optimal results. Among these, machine learning has become one of the most widely applied technologies across various domains, including healthcare, due to its ability to process large volumes of data and produce reliable predictions. One critical health problem that can benefit from these approaches is malnutrition among toddlers, which continues to pose challenges to growth, development, and long-term well-being. This analysis aims to identify the most effective and efficient algorithms for classifying the nutritional status of toddlers based on anthropometric data. The review is grounded in relevant journal articles aligned with the research topic, which serve as the primary sources for synthesis. The selected studies underwent four stages of identification, selection, evaluation, and analysis to ensure both credibility and reliability. The analysis focuses on three main aspects: dataset characteristics, algorithms applied, and outcomes reported. Based on algorithm usage, three implementation strategies were identified: single model, multi-model, and model combination. The overall findings reveal that studies utilizing datasets with fewer than 500 records can effectively apply algorithms such as Random Forest, Decision Tree, and Naïve Bayes Classifier, which consistently achieve accuracy rates above 90%. For datasets exceeding 10,000 records, the XGBoost algorithm is recommended due to its scalability and efficiency in handling large-scale data. For datasets ranging between 500 and 10,000 records, hybrid approaches such as the C4.5 algorithm combined with Particle Swarm Optimization are preferable, with previous studies demonstrating an accuracy of 94.49%. This review highlights that algorithm selection should be adjusted according to dataset size and clinical needs. The findings provide valuable insights to support researchers, practitioners, and policymakers in developing accurate and effective solutions for toddler nutrition assessment
An IoT-Enabled Bicycle Access and Monitoring System with Geo-Fence-Based Location Restriction Christian, Hans; Yusuf, Yohanes Gunawan; Ainul, Rafina Destiarti
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.123

Abstract

Managing shared bicycle usage within a university campus can be challenging, especially when relying on manual systems that are slow, difficult to track, and offer limited security. This paper propose the development of an IoT-based Bicycle Access and Monitoring System designed to make borrowing bikes easier for users and more manageable for campus administrators. The system allows users to borrow bicycles independently through a mobile application developed for Android devices. Each bicycle is equipped with a QR code, which users scan to initiate the borrowing process. After scanning, the application generates a One-Time Password (OTP), which is entered into a keypad embedded on the bicycle. After successful verification, the bicycle is automatically unlocked. To ensure bicycles remain within the designated campus boundaries, the system includes a geo-fencing feature that continuously monitors GPS coordinates. If a bicycle crosses the predefined boundary, the system triggers an alert and logs the event to a cloud-based database. All borrowing data including time, user ID, and location, are recorded in real time and accessible by campus administrators via a web interface. According experimental result show that the system functions reliably, with successful access control, accurate event logging, and an average GPS horizontal error of 1.48 meters. The proposed system provides a scalable, secure, and user-friendly solution for improving bicycle-sharing system in university area, enhancing both operational efficiency and student convenience.
Design and Implementation of an Organic and Inorganic Waste Detection System Using Capacitive, Inductive, and LDR Sensors with Rule-Based Classification Widiyasari, Diyah; Mukhtar, Husneni; Cahyadi, Willy Anugrah; Wijaya, Adhi Dharma Surya
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.133

Abstract

The continuous increase in daily waste accumulation has become a major issue in many areas, primarily due to the mixing of various waste types and the lack of effective household waste management. This complicates waste processing and contributes to environmental degradation. This study aims to design and implement a practical tool for detecting organic and inorganic waste types, specifically for use by household waste collection personnel. The developed system utilizes three sensors, capacitive, inductive, and light-dependent resistors (LDR), to acquire characteristic data from different types of waste. The device is designed in the shape of a pistol to enhance mobility and ease of use by waste collection officers. For the waste-type classification system, several machine learning methods were employed, namely Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Based on the experimental results, AdaBoost was selected as the primary model for the waste classification system because of its superior performance in terms of cross-validation accuracy and the balance of evaluation metrics, such as precision, recall, and F1-score. Consequently, AdaBoost predictions were adopted to establish a rule-based classification logic by extracting threshold values from the most influential sensor features. This study utilized AdaBoost analysis as the foundation for rule formulation, ensuring that classification decisions were based on reliable and tested data patterns. Based on testing with several samples, the device can classify organic and inorganic waste types with an accuracy rate of 91.67%. Additionally, the tool can estimate the composition of mixed waste with an error rate of 5.06%. The presence of this device has been proven to accelerate and simplify the waste-sorting process, thereby increasing the efficiency of household waste management.
Development and Acoustic Analysis of a Speaker-Output Stethoscope for Low-Cost Clinical Applications Wibowo, Kusnanto Mukti; Latif, Abdul; Susanto, Fani; Fatiatun, Fatiatun; Che Ani, Norhidayah
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.125

Abstract

This study addresses the limitations of traditional stethoscopes, which are constrained by their single-user design, dependence on auditory acuity, and susceptibility to background noise. These limitations hinder collaborative learning and diagnostic accuracy, particularly in noisy environments or during infectious disease outbreaks. The aim of this work is to develop a low-cost, speaker-output digital stethoscope that enables multiple users to simultaneously listen to heart sounds, improving both clinical training and infection control. The main contribution of this study is the integration of a conventional analog stethoscope with a high-sensitivity microphone preamplifier, an external speaker, and digital signal processing (DSP) algorithms. This configuration allows the amplification and filtering of heart sounds, enabling group auscultation without the need for earpieces. The device casing is constructed from High-Pressure Laminate (HPL) sheets and multiplex wood panels, while acoustic foam is used to reduce noise interference.  Heart sounds are captured via a microphone, amplified, and processed using Fast Fourier Transform (FFT) and band-pass filtering (20–150 Hz) to isolate the key frequencies. The system was tested in a quiet clinical setting, and the resulting audio was analyzed for clarity and frequency spectrum. The prototype successfully captured heart sounds, with a dominant spectral peak around 97 Hz, consistent with the primary frequency of heartbeats. It also clearly identified the first (S1) and second (S2) heart sounds. However, ambient noise affected sound clarity, indicating the need for further noise reduction. Despite this limitation, the device successfully enabled group auscultation. In conclusion, the speaker-output stethoscope offers an affordable and effective alternative to traditional auscultation, enhancing medical training and improving infection control. Although noise reduction requires further refinement, the system demonstrates strong potential for application in clinical and educational settings, particularly in low-resource environments
Hybrid Autoencoder Architectures with LSTM and GRU Layers for Bitcoin Price Prediction Yamasari, Yuni; Nafisah, Nurun; Yohannes, Ervin
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.132

Abstract

The high volatility of cryptocurrency markets, particularly Bitcoin, poses significant challenges for accurate price forecasting. To address this issue, this study evaluates the performance of four autoencoder-based deep learning architectures: AE-LSTM, AE-GRU, AE-LSTM-GRU, and AE-GRU-LSTM. The models were developed and tested using a univariate approach, where only the closing price was used as input, and two different window sizes (30 and 60) were applied to analyse the effect of historical sequence length on prediction accuracy. Several parameter configurations, including the number of epochs, dropout rate, and learning rate, were explored to determine the optimal model performance. The dataset comprises Bitcoin’s daily closing prices from 2018 to 2025, encompassing diverse market phases, including both bullish and bearish trends. Model performance was assessed using four evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), the coefficient of determination (R²), and Mean Absolute Percentage Error (MAPE). The results indicate that the AE-LSTM-GRU consistently achieved the best overall performance across all configurations. For a window size of 30, it achieved an RMSE of 1.53067 and a MAPE of 1.98%, while for a window size of 60, the best performance recorded was an RMSE of 1.55217 and a MAPE of 2.09%. The hybrid structure combining LSTM’s capability to capture long-term dependencies with GRU’s efficiency in information decoding demonstrated strong robustness in modelling highly volatile time series. This study contributes to financial time series forecasting by presenting hybrid autoencoder architectures that strike a balance between predictive accuracy and computational efficiency, providing practical insights for researchers and practitioners in financial technology and cryptocurrency analytics
Deep Learning-Based Hippocampal Segmentation and MTA Classification Using U-Net with ResNet-50 Backbone Salsabila, Aldienannisa Devin; Fatimah, Fatimah; Darmini, Darmini; Kurniawan, Selamet Budi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.263

Abstract

Medial Temporal Atrophy (MTA) is a key biomarker in the early diagnosis of dementia. However, its assessment through manual inspection of MRI scans is subjective, time-consuming, and prone to inter-observer variability. This creates the need for automated systems that can provide accurate, consistent, and clinically interpretable evaluations. This study aims to develop a hybrid deep learning framework that integrates U-Net with a ResNet-50 backbone for simultaneous hippocampal segmentation and MTA grading, thereby reducing diagnostic subjectivity and bridging the gap between image processing and clinical interpretation. The main contribution of this work lies in the dual functionality of the proposed architecture: not only producing precise segmentation masks of the hippocampal region but also classifying the degree of atrophy into MTA scores (0–4), which previous studies on hippocampal segmentation have not addressed. The proposed method employs a U-Net for pixel-level segmentation, enhanced with a ResNet-50 backbone to stabilize gradient propagation and enrich feature representation during encoding. Results demonstrated excellent performance, achieving a training accuracy of 99.9% with strong convergence between training and validation curves. On a test set of 32 coronal MRI slices, the model correctly classified 26 samples, misclassifying only 6. Overall, the proposed U-Net with ResNet-50 backbone provides an accurate and reliable end-to-end solution for hippocampal segmentation and MTA grading. Its clinical performance demonstrates parity with expert radiologists, underscoring its potential as a decision-support tool in dementia diagnosis. Future work will focus on extending this framework to 3D U-Net architectures, enabling the integration of volumetric MRI features to enhance robustness and generalizability across diverse datasets further

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