cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Triwiyanto
Contact Email
triwiyanto123@gmail.com
Phone
+628155126883
Journal Mail Official
triwiyanto123@gmail.com
Editorial Address
Pucang Jajar Timur No. 10, Surabaya, Indonesia
Location
Kota surabaya,
Jawa timur
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 7 Documents
 1 
Search results for , issue "Vol. 6 No. 4 (2024): November" : 7 Documents clear
Selection EEG Electrode Positions for Epilepsy Seizure Detection Using Total Power Spectrum and Machine Learning Afifah, Khilda; Istiqomah, Istiqomah; Rizal, Achamd; Nugraha, Ramdhan
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i4.9

Abstract

Detecting epileptic seizures poses significant challenges due to the complex and variable nature of EEG signals, particularly when aiming for implementation in wearable devices. The use of 64-channel EEG electrodes, while comprehensive, is impractical for wearable applications due to their size, cost, and the high computational load required for processing. The use of a single-channel EEG wearable device offers notable advantages, including reduced size and cost, making it more practical and comfortable for continuous monitoring in daily life. Additionally, the lower computational load enhances battery life and allows for real-time data processing, which is critical for timely seizure detection and intervention. This research investigates the detection of epileptic seizures using various machine learning algorithms and the power spectrum feature extraction method from EEG signals, aiming for application in wearable devices with a single-channel electrode. The study applied random forest (RF), K-nearest neighbor (KNN), decision tree (DF), support vector mechine (SVM), and logistic regression algorithms to assess their effectiveness. Results revealed that the power spectrum extraction method notably improved seizure detection accuracy, with RF and KNN achieving 93% and 92% accuracy respectively when using all EEG channels. When limited to a single channel, SVM demonstrated the highest accuracy of 82% with channel 3. These findings underscore the efficacy of the power spectrum method for EEG signal processing, providing significant improvements in accuracy and computational efficiency. The study concludes that the proposed approach is promising for enhancing epileptic seizure detection, suggesting further optimization for real-time application in wearable devices to develop accurate and efficient diagnostic tools.
Exploration of digital filters on cardiac monitor devices equipped with non-invasive blood pressure (NIBP) Nugraha, Priyambada C.; Sumber, Sumber; Muzachim, Zuva; Rabani, Rifqi; Alhaq, Elmira Rofida; Triwiyanto, Triwiyanto; Abdullayev, Vugar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/edd73780

Abstract

Heart disease is a leading cause of global mortality, making accurate monitoring essential for early detection and prevention of complications. Although heart monitoring technology has advanced, there are still limitations in precisely detecting early symptoms. This study aims to develop a Cardiac Monitor device capable of monitoring patients with heart conditions through three main parameters: electrocardiogram (ECG), phonocardiogram (PCG), and non-invasive blood pressure measurement (NIBP). The system designed in this research integrates digital filters, namely Butterworth (order 2, 4, 8) and Kalman, to enhance the quality of ECG and PCG signals. Additionally, the oscillometric method in non-invasive blood pressure measurement (NIBP) is used as a comparison for blood pressure estimation by analyzing the correlation between the R peak on the ECG signal, pulse transit time (PTT), and the first and second heart sounds (S1, S2) on the PCG signal. Blood pressure estimation is performed using algorithmic calculations to determine the accuracy of the design module in measuring systolic and diastolic pressure. The results indicate that the 8th-order Butterworth filter is more effective in reducing noise in ECG and PCG signals compared to the Kalman filter. The study also finds a significant correlation between the R peak on the ECG and the first heart sound on the PCG. The design module’s blood pressure measurement errors compared to algorithmic estimates are 4.54 ± 4.94 mmHg for systolic pressure and 6.57 ± 3.83 mmHg for diastolic pressure, which are close to the AAMI standard of 5 ± 8 mmHg. These findings highlight the great potential of the developed Cardiac Monitor device in improving early diagnosis accuracy and heart disease management.
MPPT Algorithm Based on Zebra Optimization Algorithm for Solar Panels System with Partial Shading Conditions Eviningsih, Rachma Prilian; Efendi, M. Zaenal; Windarko, Novie Ayub; Nugraha, Anggara Trisna; Prasetya, Farhan Dwi; Abdilla, M. Rafi Damas
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i4.11

Abstract

The use of solar panels is being pursued as a solution to reduce dependence on fossil fuels. However, solar panels face challenges such as power fluctuations due to environmental conditions and partial shading. To address these issues, an MPPT technique using Zebra Optimization Algorithm (ZOA) has been developed, which integrates foraging behaviour and defensive strategies to achieve GMPP. Simulation testing results show the superiority of ZOA over PSO in achieving GMPP. ZOA's contribution in addressing this problem is to efficiently perform a global search to find the optimal MPP, even under varying partial shading conditions. The algorithm mimics the behaviour of zebras in foraging and defending against predator attacks, enabling a fast solution search process and higher precision. ZOA can overcome the local maxima trap by expanding the search space, allowing solar panels to function close to optimal efficiency even if there is shading on a portion of the module. This improves system stability and performance and reduces energy loss due to partial shading. ZOA achieved a tracking accuracy of 99.99% with an average tracking time of 0.779 seconds and with a power gain of 28.5%, surpassing PSO with an accuracy of 95.18%, an average trcking time of 0.850 seconds with a power gain of 24.68%. In hardware testing, ZOA is also superior to PSO with an average tracking accuracy of 98.96% while PSO is 97.22%. These results underline the outstanding performance of the ZOA algorithm in optimising the power output of solar panels.
Development of TCR-FC Reactive Power Compensation Device with Fuzzy Logic Control in Electric Power Networks Sunarno, Epyk; Prasetyono, Eka; Anggriawan, Dimas Okky; Nugroho, Mochamad Ari Bagus; Eviningsih, Rachma Prilian; Suhariningsih, Suhariningsih; Nugraha, Anggara Trisna; Anggara Trisna Nugraha
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i4.12

Abstract

Utilization of electrical loads in predominantly inductive single-phase low-voltage power grids, the quality of electrical power becomes poor due to reactive power consumption resulting in a lack of power factor resulting in power loss, voltage drop, and decreased service life of the power grids. equipment. The research on reactive power compensation using TCR-FC aims to make improvements in improving the power factor in single-phase low-voltage electrical networks so that they have flexible control, do not experience excess compensation, have fast dynamic responses, and are space-saving. And can monitor voltage, current, and phase difference parameters through sensor readings to process data mathematically. When using electrical loads, the reactive power value is larger and the power factor is low below 0.85, the system controls the ignition angle of the TRIAC so that the current flowing into the reactor can be controlled by the reactive absorption measure of the fixed capacitor. So, it can improve the power factor. Simulation results can increase the power factor that exceeds the average value of 0.9 by 0.9797 with an error of 0.0288%. Hardware test results can increase the average power factor to exceed 0.9 by 0.9758 with an error of 0.1373%. in conclusion, reactive power compensation devices that use thyristor-controlled reactors and fixed capacitors can be more efficient than capacitor banks.
The Impactness of SMOTE as Imbalance Class Handling for Myocardial Infarction Complication Classification using Machine Learning Approach with Data Imputation and Hyperparameter Ahmad Tajali; Saragih, Triando Hamonangan; Mazdadi, Muhammad Itqan; Budiman, Irwan; Farmadi, Andi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i4.13

Abstract

Myocardial Infarction (MI) is a critical medical emergency characterized by the sudden blockage of blood flow to the heart muscle, often resulting from a blood clot in a coronary artery that has been narrowed by atherosclerotic plaque buildup. This condition demands immediate attention, as prolonged disruption of blood supply can cause irreversible damage to the heart muscle. Diagnosing MI typically involves a combination of methods, including a physical examination, electrocardiogram (ECG) analysis, blood tests to measure heart-specific enzymes, and imaging techniques such as coronary angiography. Early prediction of potential MI complications is crucial to prevent severe outcomes and improve patient prognosis. This study focuses on the early prediction of MI complications through the application of machine learning classification methods. We employed algorithms such as Support Vector Machine (SVM), Random Forest, and XGBoost to analyze patient medical records and accurately predict these complications. The selection of Support Vector Machine (SVM), Random Forest, and XGBoost in this study is driven by their proven effectiveness in handling complex classification problems. To manage incomplete datasets and preserve valuable information, data imputation techniques like K-Nearest Neighbors (KNN) Imputation, Iterative Imputation, and MissForest were applied.  KNN, Iterative, and MissForest imputations were chosen to handle missing data due to their effectiveness in preserving data integrity, which is crucial for accurate predictions in myocardial infarction complication studies. Additionally, Bayesian Optimization was utilized to fine-tune the hyperparameters of the models, thereby enhancing their predictive accuracy. The Iterative Imputation method yielded the best performance, particularly in SVM and XGBoost algorithms. SVM achieved 100% accuracy, precision, sensitivity, F1 score, and Area Under the Curve (AUC), while XGBoost attained 99.4% accuracy, 100% precision, 79.6% sensitivity, an F1 score of 88.7%, and an AUC of 0.898. While XGBoost and MissForest proved to be the most successful pairing, the overall effectiveness of the models suggests that Iterative Imputation and Random Forest also have potential under certain conditions.
Control Watering Nutrients for Rice Plants With a Drip Irrigation System Using Arduino and RTC Haryanti, Tri; Fitriyanto, Indra; Maknunah, Jauharotul
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v6i4.14

Abstract

Indramayu Regency is one of the largest rice-producing areas in West Java, but its rice productivity, averaging 6 tonnes per hectare, falls short of the national target of 8 tonnes per hectare. One key problem contributing to this gap is the nutrient imbalance in rice plants, which can lead to pest and disease attacks. To address this, an automated drip irrigation system was developed to optimize nutrient delivery and improve crop yield. The aim of this study was to design a nutrient control system for hydroponic rice using Arduino and an RTC (Real-Time Clock), allowing for precise and scheduled watering of nutrients. The system contributes to better plant health, higher crop productivity, and more efficient use of water and nutrients. The method involved testing eight water pumps, each controlling different nutrient doses, in a hydroponic rice planting system. The system was set to irrigate according to a programmed schedule, with pumps activated at specific times to deliver nutrients to rice plants grown in polybags with fine sand. Data collection was conducted over 30 days by analyzing the condition of rice plant leaves for each nutrient dose. The results showed that the drip irrigation system successfully controlled nutrient delivery, and the analysis identified the optimal nutrient dose for healthy plant growth. In conclusion, the automated drip irrigation system using Arduino and RTC not only improved water and nutrient efficiency but also contributed to higher rice yield quality and quantity. The system shows promise for reducing production costs by lowering water and fertilizer usage while minimizing environmental impact.
Classification of brain tumor based on shape and texture features and machine learning Rizki, M. Alfi; Faisal, Mohammad Reza; Farmadi, Andi; Saragih, Triando Hamonangan; Nugrahadi, Dodon Turianto; Bachtiar, Adam Mukharil; Keswani, Ryan Rhiveldi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 4 (2024): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/27236g49

Abstract

Information from brain tumour visualisation using MRI can be used for brain tumour classification. The information can be extracted using different feature extraction techniques. This study compares shape-based feature extraction such as Zernike Moment (ZM), and Pyramid Histogram of Oriented Gradients (PHOG) with texture-based feature extraction such as Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG) in brain tumour classification. This research aims to find out which feature extraction is better for handling brain tumour images through the accuracy and f1-score produced. This research proposes to combine each feature based on its approach, i.e. ZM+PHOG for shape-based feature extraction and LBP+GLCM+HOG for texture-based feature extraction with default parameters from the library and modified parameters configured based on previous research. The dataset used comes from Kaggle and has three classes: meningioma, glioma, and pituitary. The machine learning classification models used are Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB) and K-Nearest Neighbours (KNN) with default parameters from the library. The models were evaluated using 10-fold stratified cross-validation. This research resulted in an accuracy and f1-score of 84% for texture-based feature extraction with modified parameters in RF classification. In comparison, shape-based feature extraction resulted in accuracy and f1-score of 70% and 68% with modified parameters in RF classification. From the results, it can be concluded that texture-based feature extraction is better in handling brain tumour images compared to shape-based feature extraction. This study suggests that focusing on texture details in feature extraction can significantly improve classification performance in medical imaging such as brain tumours

Page 1 of 1 | Total Record : 7

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026): February Vol. 7 No. 4 (2025): November Vol. 7 No. 3 (2025): August Vol. 7 No. 2 (2025): May Vol. 7 No. 1 (2025): February Vol. 6 No. 4 (2024): November Vol. 6 No. 3 (2024): August Vol. 6 No. 2 (2024): May Vol. 6 No. 1 (2024): February Vol. 5 No. 4 (2023): November Vol. 5 No. 3 (2023): August Vol. 5 No. 2 (2023): May Vol. 5 No. 1 (2023): February Vol. 4 No. 4 (2022): November Vol. 4 No. 3 (2022): August Vol. 4 No. 2 (2022): May Vol. 4 No. 1 (2022): February Vol. 3 No. 4 (2021): November Vol. 3 No. 3 (2021): August Vol. 3 No. 2 (2021): May Vol. 3 No. 1 (2021): February Vol. 2 No. 3 (2020): November Vol. 2 No. 2 (2020): August Vol. 2 No. 1 (2020): February Vol. 1 No. 2 (2019): November Vol. 1 No. 1 (2019): August More Issue