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Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya Jl. Pucang Jajar Timur No. 10, Surabaya, Indonesia
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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 9 Documents
 1 
Search results for , issue "Vol 5 No 3 (2023): July" : 9 Documents clear
Non-Communicable Diseases (NCDs) Information System Farid Agushybana; Cahya Tri Purnami; Dharminto
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i3.292

Abstract

Non-communicable diseases (NCDs) are the leading cause of death worldwide. In Indonesia, NCDs has increased and shifted patterns. Changes in behavior and lifestyle increase the risk of NCDs. Difficulties in monitoring treatment and patient visits are a challenge for health workers. Currently, there is still no integrated information system for patient monitoring. The general objective of this research is to develop an integrated Non-Communicable Disease Information System (NCDSI) in Semarang City. The current study uses the Rapid Application Development (RAD) method with a qualitative approach. RAD is a complete approach model for information system development and covers the entire system life cycle. This research was conducted in three stages: planning and needs analysis, designing, developing, and collecting feedback. During the development and collection of feedback, functionality testing activities are carried out to obtain feedback and evaluation as the system is improved. The research was carried out by the Semarang City Office. The functional test subjects consisted of three agents and five patients with primary hypertension and diabetes mellitus. The results: the planning and needs analysis phases were carried out through thorough interviews with end-users. The system design phase was described through contextual diagrams and database tables, and the development and feedback phase was conducted through the NCDSI functionality test. Conclusion: The developed NCDSI has been integrated and has four user levels (users of the City Health Office, Head of Puskesmas, Cadres, and patients).
Single Objective Mayfly Algorithm with Balancing Parameter for Multiple Traveling Salesman Problem YOGA DWI WAHYU NUGRAHA; HENDRAWAN ARMANTO; YOSI KRISTIAN
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.299

Abstract

The Multiple Travelling Salesman Problem (MTSP) is a challenging combinatorial problem that involves multiple salesman visiting a set of cities, each exactly once, starting and ending at the same depot. The aim is to determine the optimal route with minimal cost and node cuts for each salesman while ensuring that at least one salesman visits each city. As the problem is NP-Hard, a single-objective metaheuristic algorithm, called the Mayfly Algorithm, inspired by the collective behavior of mayflies, is employed to solve the problem using the TSPlib95 test data. Since the Mayfly Algorithm employs a single fitness function, a balancing parameter is added to perform multiobjective optimization. Three balancing parameters in the optimization process: SumRoute represents the total cost of all salesmen travelling, StdRoute balances each salesman cost, and StdNodes balances the number of nodes for each salesman. The values of these parameters are determined based on the results of various tests, as they significantly impact the MTSP optimization process. With the appropriate parameter values, the single-objective Mayfly Algorithm can produce optimal solutions and avoid premature convergence. Overall, the Mayfly Algorithm shows promise as a practical approach to solving the MTSP problem. Using multiobjective optimization with balancing parameters enables the algorithm to achieve optimal results and avoid convergence issues. The TSPlib95 dataset provides a robust testing ground for evaluating the algorithm’s effectiveness, demonstrating its ability to solve MTSP effectively with multiple salesman.
From Imaging Data to Cranioplasty Implant Designs Asmaria, Talitha; Zain, Andi Justike Mahatmala; Pramesti, Arindha Reni; Marzuki, Azwien Niezam Hawalie; Utomo, Muhammad Satrio
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.300

Abstract

The cranioplasty procedure is starting from removal the skull bone defects and replacing them with any biocompatible material, such as polymer, ceramic, or titanium alloy. The complication of the surgery as well as the high cost from several material selection required a simulation. Besides that, the case of cranial defects sometimes required a customized design. The presence of three-dimensional (3D) printing technology would be a promising tool to improve the success rate. Prior to 3D printing, the model needs to be corrected from the initial patient’s imaging data to the intended implant design. However, previous related literatures were almost not informing the specific image processing steps to gain the models, while not all operators could understand this sophisticated technique. The study aims to design an implant bone for cranioplasty purpose. The data were processed through the very clear step-by-step image processing stages, three-dimensional (3D) printing, and its evaluation through biomechanical simulation. Quantitatively, the designed cranioplasty implant could deal with the load in the actual application. Qualitatively, the prototypes have matched if applied to the host of cranium bone. In conclusion, although image processing and refinements are the most complicated process, the whole explanation indicate that the provided precise methodology could be a major reference to the similar procedure.
Comparison of the performance of linear discriminant analysis and binary logistic regression applied to risk factors for mortality in Ebola virus disease patients Leader Lawanga Ontshick; Jean-Christophe Mulangu Sabue; Placide Mbala Kiangebeni; Olivier Tshiani Mbayi; Jean-Michel Nsengi Ntamabyaliro; Jean-jacque Muyembe Tamfumu; Rostin Mabela Makengo Matendo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i3.303

Abstract

Our study aimed to identify risk factors associated with mortality in Ebola patients using binary logistic regression analysis and linear discriminant analysis, and to assess the predictive power of these two methods. Our study was a randomized, double-blind, controlled (observational) clinical trial conducted in 2018 during the 10th Ebola outbreak in eastern DRC. The study included 363 patients divided into two treatment arms, including 182 patients treated with MAB114 (Ebang) and 181 patients treated with REGENERON (REGN-EB3 ). After a thorough analysis of the data, both statistical analysis methods selected the same set of variables (risk factors) for the binary logistic regression we obtained: viral load 0.58 (0.5-0.67), creatinine 1.98 (1.58-0.67) and aspartate aminotransferase 0.99 (0.9-1); as for the linear discriminant analysis we have viral load (0.88), creatinine (0.94) and aspartate aminotransferase (0.78). We also see almost the same results when different prediction probabilities are evaluated. Logistic regression predicted a mortality rate of 36.5% and linear discriminant analysis predicted a mortality rate of 38.8%. Using the AUC (area under the curve) score, we were able to evaluate two methods and obtain a score of 0.935 for the binary logistic regression and 0.932 for the linear discriminant analysis. According to the evaluation hypothesis, both methods give the same risk factors (viral load (Ctnp), creatinine and alanine aminotransferase (ALT)) with a probability of 93%.
Sentiment Analysis on Satusehat Application Using Support Vector Machine Method Shahmirul Hafizullah Imanuddin; Kusworo Adi; Rahmat Gernowo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.304

Abstract

Sentiment analysis is important in language processing and machine learning. SVM is proven to classify positive and negative sentiments with high accuracy effectively. SatuSehat application provides users with various health services and medical information, previously known as the PeduliLindungi Application. Once, this application was used to handle vaccination history used in the new normal era. Along the way, many problems arose due to the immaturity of the application after it was launched, which resulted in many user reviews being given through the Google Play Store application. Therefore, this study aims to determine SVM's performance in classifying user reviews of the SatuSehat application into positive and negative sentiments and to show visualization to find out the most frequent words from user reviews. Based on the research results, 25,000 data were divided into 18,359 negative class data and 6,641 positive class data. At the SVM classification stage, it produces a negative sentiment of 73.4% and a positive sentiment of 26.6%. In addition, the results of the SVM accuracy test obtained a result of 91% with a positive sentiment, namely having a precision test of 92%, a recall of 71%, and an f1-score of 80%, while for negative sentiment, namely having a precision test of 90%, a recall of 98% and f1-score of 94%. The visualization results found that the topics often appearing in positive reviews are good and sometimes great. In contrast, the negative reviews are update, difficult, strange, login, and bug.
Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification Shalehah; Muhammad Itqan Mazdadi; Andi Farmadi; Dwi Kartini; Muliadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.305

Abstract

Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.
Development of Stride Detection System for Helping Stroke Walking Training Ilham Ari Elbaith Zaeni; Dyah Lestari; Anik N. Handayani; Muhammad Khusairi Osman
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.306

Abstract

Walking is a popular post-stroke rehabilitation exercise for patients. Stroke walking training is a sort of physical therapy that aims to help people who have had a stroke improve their walking ability. The goal of this research is to classify stride length and include it into a mobile application. The accelerometer sensor on a smartphone can be used to construct a stride detection system to aid in stroke walking training. This application was created for Android-powered smartphones. A binder must be used to secure the smartphone device to the patient's thigh. This application reads the accelerometer sensor included into the smartphone. In this study, a stride detection model is designed to increase the performance of stride length and circumduction detection. The accelerometer is read and saved by the application as the participant walks on the specific path. After the signal has been pre-processed and its feature extracted, the data is used to create the stride detection model. The performance is good, as evidenced by accuracy, precision, recall, and f-measure values of 88.60%, 88.60%, 88.60%, and 88.60%, respectively. When utilized on a stride detection system, the decision tree algorithms function admirably. The model is then loaded into the Android walking app.
Network-Based Molecular Features Selection to Predict the Drug Synergy in Cancer Cells Syarifah Aini; Wisnu Ananta Kusuma; Medria Kusuma Dewi Hardhienata; Mushthofa
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.307

Abstract

Identifying synergistic drug combinations in cancer treatment is challenging due to the complex molecular circuitry of cancer and the exponentially increasing number of drugs. Therefore, computational approaches for predicting drug synergy are crucial in guiding experimental efforts toward finding rational combination therapies. This research selects the molecular features of cancer cells with a diffusion network-based approach. Additionally, a model is developed using non-linear regression algorithms, namely Random Forest, Extremely Randomized Tree, and XGBoost, to predict the synergy score of drug combinations against the selected cancer cell features. The data used are drug combination screening data and cancer cell molecules provided by AstraZeneca-Sanger DREAM Challenge. The feature selection results demonstrate the relevance of cancer cell molecular features selected by the diffusion network. The prediction results indicate that the Random Forest algorithm shows a good correlation value of 0.570 in the model with a small dataset. In contrast, for the model with an instance or row size larger than the number of features or columns, the XGBoost algorithm achieves a good correlation value of 0.932. INDEX TERMS cancer, drug combination, drug synergy, network diffusion kernel, non-linear regression.
Parameter Optimization of Support Vector Machine using River Formation Dynamic on Brain Tumor Classification Cahya Kemila, Azizah; Fawwaz Al Maki, Wikky
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i3.312

Abstract

Brain tumor is a condition that can interfece with brain function due to abnormal cell growth in the brain. MRI is used as a diagnostic tool when a patient has a brain tumor. The images obtained through MRI will be analyzed by the doctor to determine the type of tumor. Therefore, it is necessary to have a system that can classify tumor types based on MRI images. The image will be extracted using the HOG method, then classified using SVM. Certain measures can be taken to improve SVM performance, such as optimizing its parameters. This research develops a system that uses a novel combination, the SVM with the River Formation Dynamic (RFD) algorithm. RFD is being used to optimize parameters of SVM (C and gamma). The basic idea of RFD is to imitate the movement of water droplets flowing from high to low areas. This research compares the accuracy produced by SVM with the accuracy produced by SVM-RFD. The result is that SVM-RFD provides the better accuracy than only using SVM. The accuracy result by SVM on the MRI dataset is 74.37%. When we compared it with SVM-RFD, the accuracy increased by 13.19% to 87.56%. Further work will be carried out on the implementation of RFD on other SVM parameters to find other parameter combinations that can improve the accuracy of SVM.

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