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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
Thermal Image Classification of Autistic Children Using Res-Net Architecture Ahmadiar, Ahmadiar; Melinda, Melinda; Muthiah, Zharifah; Zainal, Zulfan; Mina Rizky, Muharratul
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: 10.35882/365fkd59

Abstract

The thermal Image Classification Method has been widely used for significant applications in many fields, including thermal images of the face. This study presents a method for thermal facial classification in children with autism spectrum disorder (ASD). Children with ASD have a neurological disorder that affects communication skills essential in daily life and often causes difficulties in social situations. As we know, the diagnosis of ASD currently still relies on human methods and does not yet have definite biological markers. Early diagnosis of ASD has a significant positive impact, especially in children. Deep learning techniques, especially in facial medical image analysis, have become a new research focus in ASD detection. Initial screening using a Convolutional Neural Network (CNN) model with a transfer learning approach offers great potential for early diagnosis of ASD. The use of thermal imaging as a passive method to analyze ASD-related physiological signals has been proposed. In previous research, a deep learning model was developed to classify the faces of autistic children using thermal images. Therefore, this study aims to create a new Thermal Image Classification model for Autistic Children Using Res-Net Architecture. The architectures applied are ResNet-18, ResNet-34, and ResNet-50. As a comparison system, several of the same parameter values are used: epoch 100, batch size 2, SGD, Cross-entropy, learning rate 0.001, and momentum 0.9. The study test results show that the results of ResNet-18 are 97.22%, ResNet-34 99.22%, and ResNet-50 99.41%. Based on these results, ResNet-50 has the highest value.
LoRA-LoRaWAN Communication Multinode for 3D Localization in Coastal Environment Musayyanah, Musayyanah; Pauladie Susanto; Pradita Maulidya Efendi; Charisma Dimas Affandi; Kristin Lebdaningrum; Theodorus Visser Inulima
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/xv8hwc75

Abstract

The application of LoRAWAN on Internet of Things (IoT) technology is  to communicate in real time and accommodate data from many nodes based on device addresses. LoRAWAN device communication is able to reach distances of up to kilometers with low cost, compared to high-frequency cellular communication currently installed on the coast. This application can be done on LoRA devices to forward the results of three-dimensional localization based on signal strength. This research was  to conduct three-dimensional localization based on signal strength from three LoRa End Nodes (EN) to four LoRa Anchor Nodes (AN), then forwarded to the server to be displayed on the Datacakes application. Localization begins with a path loss model analysis to determine the path loss coefficient. The localization results were  in the form of data packets consisting of longitude, latitude, and altitude position parameters and the results of the EN to AN distance conversion. The data packet was  forwarded to the The Things Networks (TNN) server with Over The Air Activation (OTAA) activation mode. Root Mean Square Error (RMSE) analysis of the localization results for EN1 was  169.35 meters, EN2 was  395.08 meters and EN3 was  183.24 meters. The localization data packets were  forwarded to the cloud server via the GW device. Analysis of GW communication with EN is shown by the Packet Error Ratio (PER), Air Time (AT), and latency parameters. The smallest PER results, fastest AT and lowest latency were  obtained from GW communication with EN2, where the position of EN2 was  closest to GW among the other ENs.
Bitcoin Mining Hardware Profitability Prediction Using Categorical Boosting and Extreme Gradient Boosting Algorithms Dimas Satria Prayoga; Puspita Sari, Anggraini; Junaidi, Achmad
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: 10.35882/9xb2dz14

Abstract

Cryptocurrencies, especially Bitcoin, have gained global recognition, with mining being one of its most interesting aspects. This is especially important in the context where only a few types of bitcoin mining rigs are expected to operate profitably. On the other hand, in the field of machine learning, there are widely used algorithms, namely Extreme Gradient Boosting (XGBoost), which is known for its effectiveness, and Categorical Boosting (CatBoost), which excels in handling categorical data. This study aims to combine the performance of CatBoost and XGBoost using the Ridge Regression technique in predicting a case study that is not often encountered, namely predicting the profitability of Bitcoin mining hardware. The main steps include collecting data from reliable sources, preprocessing the data to ensure compatibility, feature selection to select the most relevant features, building a prediction model using the preprocessed data set, and then training and testing both models to evaluate their predictive accuracy. The evaluation metrics on the test data reveal the performance of CatBoost, XGBoost, and the CatBoost-XGBoost. CatBoost demonstrates a training time of 3.35 seconds with a MAPE of 15.67% and an RMSE of 0.1733. In comparison, XGBoost has a longer training time of 5.27 seconds but achieves a significantly lower MAPE of 6.49% and an RMSE of 0.1737. Meanwhile, the CatBoost-XGBoost, with the longest training time of 6.84 seconds, delivers a competitive MAPE of 6.57% and the lowest RMSE of 0.1696 among the three approaches. These results highlight that while XGBoost and CatBoost meta model outperform CatBoost in terms of accuracy, the Ridge meta model provides slightly better overall predictive performance based on RMSE.
Examining the Relationship between Water-Equivalent Diameter (Dw) and Body Mass in Breast Cancer Patients Nurhanivah, Devi; Ramdhani, Saumi Zikriani; Bilqis, Ayesha
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: 10.35882/3g7sep81

Abstract

Breast cancer is the most prevalent cancer worldwide, necessitating precise imaging techniques for effective treatment planning. This study aims to analyze the Water-Equivalent Diameter (Dw) in breast cancer patients using Computed Tomography (CT) and to investigate its relationship with patient body mass. Medical imaging data from 30 breast cancer patients, aged 23-66 years, was reviewed to calculate Dw using three different methods: contour ROI, elliptical ROI, and without ROI. The results showed average Dw values of 28.68 cm for contour ROI, 29.184 cm for elliptical ROI, and 30.255 cm without ROI. This indicates that contour ROI yields the smallest Dw due to its focus on cancerous areas. Furthermore, a positive linear correlation between Dw and body mass was established, with an R² value of 0.7743. This suggests that larger body mass leads to increased Dw values. This study emphasizes the importance of considering ROI selection and highlights the significant impact of patient body mass on Dw. This is crucial for optimizing radiation exposure in breast cancer treatment.
Forecasting Electricity Consumption in Riau Province Using the Artificial Neural Network (ANN) Feed Forward Backpropagation Algorithm for the 2024-2027 Tengku Reza Suka Alaqsa; Zulfatri Aini; Liliana; Nanda Putri Miefthawati
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: 10.35882/7eeq7029

Abstract

Electricity production in Riau Province fluctuates between surplus and deficit, as reported by the Central Statistics Agency. From a peak of 3,758.75 GWh in 2017, production fell to 525.19 GWh in 2019, mainly due to lack of investment in new power plants and dependence on external electricity supply. This study addresses these challenges by using the Artificial Neural Network (ANN) Feed Forward Backpropagation method to forecast electricity demand from 2024 to 2027. This study aims to analyze the accuracy of the prediction through the Mean Absolute Percentage Error (MAPE), evaluate electricity consumption projections, and calculate the annual growth rate. The gap in this study is the inclusion of previously ignored variables, namely the GRDP of Government Buildings and the number of Government Building customers. The methodology used is Artificial Neural Network Feed Forward Backpropagation. In the training data training, the MAPE was obtained at 4,315%. The electricity consumption prediction obtained is 8,679 GWh in 2024, 9,690 GWh in 2025, 10,959 GWh in 2026, and 12,681 GWh in 2027. The growth rate is also projected to increase, namely 5.67% from 2023 to 2024, 11.65% from 2024 to 2025, 13.10% from 2025 to 2026, and 15.71% from 2026 to 2027.
Examining the Influence of e-Health Literacy on Healthcare Workers’ Acceptance of Electronic Medical Records: An Insight Into System Transition Noeryosan, Ilham; Arini, Merita; Binti Wan Mamat, Wan Hasliza
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/b160n685

Abstract

Following the aftermath of COVID-19, needs of digitalized medical data system has increased worldwide. As stated by Indonesia’s Ministry of Health, electronic medical records (EMRs) usage are being mandated initially by December 2023. However, in some healthcare facilities, this transition are being halted by inadequate digital literacy. This research aimed to understand the impact of e-Health Literacy of healthcare personnel on technology acceptance and their intention to use EMRs.  The cross-sectional study was conducted in March 2024 following six months of EMR implementation in Dr. Soetarto Army Hospital, using a valid and reliable questionnaire consisting of 51 items originating from the Unified Theory of Acceptance and Use of Technology (UTAUT) and e-Health Literacy Questionnaire (eHLQ) that was modified into 46 items. The data was collected from 114 healthcare personnel who act as both caregiver and medical data documenter (total sampling). The result was then analyzed using Smart-PLS. There is an increased intention of EMR usage when e-health literacy moderated user's technology acceptance (p=0.006), while by itself, technology acceptance doesn't have a meaningful impact towards intention to use EMR (p=0.391). Increased e-Health Literacy has also proven to be correlated with increased intention of EMR use (p<0.001). Increasing user’s e-health literacy is essential to become a pivotal factor in increasing EMR adoption in healthcare personnel workflow. This study suggests integrating targeted e-health literacy programs into professional development to improve EMR usage and healthcare efficiency, with future studies exploring long-term.
Enhancing Natural Disaster Monitoring: A Deep Learning Approach to Social Media Analysis Using Indonesian BERT Variants Fitriani, Karlina Elreine; Faisal, Mohammad Reza; Mazdadi, Muhammad Itqan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Prastya, Septyan Eka
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/t158qq37

Abstract

Social media has become a primary source of real-time information that can be leveraged by artificial intelligence to identify relevant messages, thereby enhancing disaster management. The rapid dissemination of disaster-related information through social media allows authorities to respond to emergencies more effectively. However, filtering and accurately categorizing these messages remains a challenge due to the vast amount of unstructured data that must be processed efficiently. This study compares the performance of IndoRoBERTa, IndoRoBERTa MLM, IndoDistilBERT, and IndoDistilBERT MLM in classifying social media messages about natural disasters into three categories: eyewitness, non-eyewitness, and don’t know. Additionally, this study analyzes the impact of batch size on model performance to determine the optimal batch size for each type of disaster dataset. The dataset used in this study consists of 1000 messages per category related to natural disasters in the Indonesian language, ensuring sufficient data diversity. The results show that IndoDistilBERT achieved the highest accuracy of 81.22%, followed by IndoDistilBERT MLM at 80.83%, IndoRoBERTa at 79.17%, and IndoRoBERTa MLM at 78.72%. Compared to previous studies, this study demonstrates a significant improvement in classification accuracy and model efficiency, making it more reliable for real-world disaster monitoring. Pre-training with MLM enhances IndoRoBERTa’s sensitivity and IndoDistilBERT’s specificity, allowing both models to better understand context and optimize classification results. Additionally, this study identifies the optimal batch sizes for each disaster dataset: 32 for floods, 128 for earthquakes, and 256 for forest fires, contributing to improved model performance. These findings confirm that this approach significantly improves classification accuracy, supporting the development of machine learning-based early warning systems for disaster management. This study highlights the potential for further model optimization to enhance real-time disaster response and improve public safety measures more effectively and efficiently.
Development and Optimization of an Ultrasound-Assisted Extraction Apparatus with Integrated Advanced Features for Enhanced Essential Oil Production Fatmasari, Diyah; Widyawati, Melyana Nurul; Amartha, Tecky Afifah Santy; Fathonah, Yayuk; Aquarista, Nita; Amalia, Dhanty Nurul
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: 10.35882/11vb2734

Abstract

The development of essential oil extraction techniques has seen significant advancements with the introduction of Ultrasound-Assisted Extraction (UAE) technology. UAE is known for its ability to accelerate chemical processes and improve the efficiency of essential oil extraction. However, to achieve optimal extraction quality and yield, precise control over various process parameters is required. This study focuses on enhancing the UAE extraction process by integrating modified features, including a temperature sensor, speed sensor, frequency regulator, temperature controller, stirrer, and cooler, into the extraction apparatus. Objective of this research is to develop a UAE-based extraction apparatus with advanced features that optimize the pre-treatment extraction process of essential oils. The research employs a Research and Development (R&D) approach to design and develop the UAE-based extraction apparatus. The apparatus is then tested to evaluate its performance in optimizing the extraction process. Experimental trials are conducted to assess the impact of each feature on the efficiency and quality of the extracted essential oils. The integration of the temperature sensor, speed sensor, frequency regulator, temperature controller, stirrer, and cooler allowed for better control over the extraction conditions, leading to higher yield and improved quality of the essential oils. The apparatus demonstrated its ability to consistently produce essential oils with higher purity and active ingredient concentrations compared to conventional methods. In conclusion, the development of the modified UAE-based extraction apparatus proves to be an effective solution for optimizing the essential oil extraction process
Innovation in devices for Newborn Stability During transport : A Systematic Literature Review Widyawati, Melyana Nurul
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/fxgzje43

Abstract

Goal: The development of devices for newborn stability during transport has gained attention, particularly in neonatal care. These devices, with real-time monitoring, aim to reduce risks like hypothermia, respiratory distress, and cardiovascular instability. This systematic literature review examines the current innovations in newborn stabilization devices, exploring their design, functionalities, and the integration of monitoring systems to enhance neonatal outcomes. The review provides an overview of device advancements, assesses their effectiveness in maintaining newborn stability, and discusses the benefits, limitations, and future challenges in their broader application. These innovations hold the potential to revolutionize neonatal transport care, improving safety and survival rates for high-risk infants globally.
Development of Medical Record Technology and Information Systems on the Performance of RSU Pacitan Employees sutanto, warkim; Arini, Merita
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/ta5qv525

Abstract

The development of Electronic Medical Record (RME) technology and information systems is an important need in the health sector to improve operational efficiency and service quality. This research analyzes the effect of implementing RME, technology user training, IT infrastructure, and data security and privacy on the performance of RSU Pacitan employees. Using quantitative methods with a cross-sectional design, data was collected from 30 respondents via a Google Form questionnaire and analyzed using SPSS. The results show that the four variables have a significant effect on employee performance (R² = 0.878), with the implementation of RME (coefficient 0.232) making administrative services easier, technology training (0.722) increasing employee self-confidence, and IT infrastructure (0.339) supporting productivity. Meanwhile, data security and privacy (-0.376) have a moderate influence because they play a more significant role in creating a safe work environment. This research confirms the importance of implementing EMR in supporting employee performance and recommends further research to analyze other aspects of EMR, as well as becoming the basis for hospital digital transformation policies.

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