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 199 Documents
 2   3   4   5   6 
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.
Application of Heuristic Algorithm in Medical Informatics System for Patient Data Management in Southeast Asia Setyani, Seswiyati Asri; Mahendro Prasetyo Kusumo
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

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

Abstract

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

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

Abstract

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

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

Abstract

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

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

Abstract

Agriculture is a key contributor to Indonesia's economic growth, with tubers representing the second most important food crop. Despite their significance, the export value of Indonesia’s tuber crops has not yet reached its full potential given the decline in the value of tuber exports since 2021. One of the contributing factors is the restricted range of export market options. This study aims to analyze export trade patterns to identify the most high-potential markets for Indonesian tuber commodities.  Clustering analysis is used as a key method to identify market locations by grouping countries based on similar trade characteristics. Clustering was conducted using the Gaussian Mixture Model (GMM), which enhanced by Particle Swarm Optimization (PSO) and evaluated by silhouette score and DBI. The dataset is collected from Indonesia’s Central Bureau of Statistics from 2019 to 2023, focusing on 5 kinds of tuber exports with total of 455 entries and 8 columns. Using the AIC/BIC method, the optimal number of clusters obtained is 2 which are low market opportunities (cluster 0) and high market oppurtunities (cluster 1). Results showed that the GMM model without optimization has silhouette score of 0.7602 and DBI of 0.8398, while the GMM+PSO model achieved an improved silhouette score of 0.8884 and DBI of 0.5584. Both score are categorized as strong structure but, GMM+PSO has higher silhouette score and lower DBI score, demonstrating the effectiveness of PSO in enhancing the clustering model’s performance. The key potential markets for Indonesian tuber exports are primarily concentrated in Asia, including countries such as China, Malaysia, Thailand, Vietnam, Hong Kong, and United States.

Page 4 of 20 | Total Record : 199

 2   3   4   5   6 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue 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