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International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE)
Published by Universitas Indonesia
ISSN : -     EISSN : 30265258     DOI : https://doi.org/10.62146/ijecbe.v2i1
Core Subject : Health, Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Materials Science & Nanotechnology Medicine & Pharmacology
The International Journal of Electrical, Computer, and Biomedical Engineering (IJECBE) is an international journal that is the bridge for publishing research results in electrical, computer, and biomedical engineering. The journal is published bi-annually by the Electrical Engineering Department, Faculty of Engineering, Universitas Indonesia. All papers will be blind-reviewed. Accepted papers will be available online (free access) The journal publishes original papers which cover but is not limited to Electronics and Nanoelectronicsc Nanoelectronics and nanophotonic devices; Nano and microelectromechanical systems (NEMS/MEMS); Nanomaterials; Quantum information and computation; Electronics circuits, systems on chips, RF electronics, and RFID; Imaging and sensing technologies; Innovative teaching and learning mechanism in nanotechnology education; Nanotechnologies for medical applications. Electrical Engineering Antennas, microwave, terahertz wave, photonics systems, and free-space optical communications; Broadband communications: RF wireless and fiber optics; Telecommunication Engineering; Power and energy, power electronics, renewable energy source, and system; Intelligent Robotics, autonomous vehicles systems, and advanced control systems; Computational Engineering. Computer Engineering Architecture, Compiler Optimization, and Embedded Systems; Networks, Distributed Systems, and Security; High-performance Computing; Human-Computer Interaction (HCI); Robotics and Artificial Intelligence; Software Engineering and Programming Language; Signal and Image Processing. Biomedical Engineering Cell and Tissue Engineering; Biomaterial; Biomedical Instrumentation; Medical Imaging.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 8 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2023)" : 8 Documents clear
Multichannel Slotted ALOHA Simulator Design for Massive Machine-Type Communication (mMTC) on 5G Network Feliana, Ferlinda; Harwahyu, Ruki; Overbeek, Marlinda Vasty
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.8

Abstract

Massive Machine-type Communication (mMTC) is one of the main service scenarios in 5G. At the time of initializing the connection to the base station, the MTC machines will make a connection request via the random access procedure. One of the schemes of random access procedure for handling this connection request is similar to how multichannel slotted ALOHA works. Multichannel slotted ALOHA itself is a development of the slotted ALOHA scheme which originally has only a single channel. At the initial state of mMTC, there will be an explosion of the number of demands to the available channels. Given the number of machines that will be connected, the likelihood of a collision on the same channel increases. As a result, the probability of failure also increases. The system's configuration has an impact on the likelihood of success and the time it takes to achieve it. The number of channels influences the likelihood of collisions, the backoff window influences the transmission distribution in each slot, and the maximum transmission limits the ability of device retransmission. These three arrangements have an impact on one another. The simulator build in this research is expected to make it easier for researchers to optimize multichannel slotted ALOHA configurations in 5G to handle the surge in access demands from mMTC devices.
Web Application Development Skin Lesion Classification Using Transfer Learning InceptionResNet-v2 Harahap, Nanda Ilham; Zulkifli, Fitri Yuli
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.13

Abstract

The development of machine learning continues from various domains where automation systems are needed. Advanced learning models, such as Convolutional Neural Networks (CNNs) in deep learning, can classify and identify objects even beyond human capabilities. One application is the classification of medical images skin cancer. Automatic diagnosis of skin cancer images is still challenging for CNNs. The use of transfer learning on classification has been leveraged for mobile, accurate, and fast automatic diagnosis. However, such models are imperfect in the categorization of skin lesions. Therefore, this study developed a web application for multiclass classification of 7 classes of disease through Streamlit and HuggingFace, with datasets from HAM10000 using TF Lite-conversion InceptionResNetV2. TF Lite-converted and the model’s classification reports were analyzed. The results on EarlyStopping overall accuracy were 87.56%, top-2 95.05%, and top-3 97.46%. Moreover, latency and classification duration were measured on Streamlit Share and HuggingFace Spaces. The findings are Streamlit has a faster average latency (1.17 ms) than HuggingFace (1.49 ms). The latency standard deviation on HuggingFace less consitent (0.49 ms) than Streamlit (0.10 ms). The HuggingFace classification average duration and standard deviation is 116 ms and 5 ms, while Streamlit is better at 97 ms and 2 ms respectively.
Implementation of Xception and EfficientNetB3 for COVID-19 Detection on Chest X-Ray Image via Transfer Learning Novalina, Nadya; Rizkinia, Mia
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.14

Abstract

COVID-19 is a highly contagious infectious disease caused by the SARS-CoV-2 virus that can cause respiratory issues. The utilization of X-ray imaging has the potential to serve as an alternative means of detecting COVID-19 by offering insights into the condition of the lungs. Rapid and automated analysis of medical images and patterns can be achieved through deep learning techniques. In this study, we propose methods for the automatic classification of COVID-19 from Chest X-Ray images using CNN with transfer learning techniques, namely Xception and EfficientNetB3 architectures, as well as an ensemble of both architectures working in parallel. Additionally, we use Grad-CAM to visualize the regions of the X-ray image that are most important for the classification decision. The classification of COVID-19 is carried out for four types of classes: COVID-19, normal, bacterial pneumonia, and viral pneumonia. The proposed classifier models achieve an overall accuracy of 94.44% for the Xception classifier, 95.28% for the EfficientNetB3 classifier, and 94.44% for the parallel classifier. The accuracy value is higher than the other comparison classifiers accuracy values. Overall, the proposed classifiers can be recommended as tools to assist radiologists and clinical practitioners in diagnosing and following up with COVID-19 cases.
Fabrication of Organic Light Emitting Diodes (OLEDs) using the Lamination method in a Vacuum-Free Environment Alfafa, Daris; Moraru, Daniel; Udhiarto, Arief
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.24

Abstract

Organic Light Emitting Diodes (OLEDs) have recently become one of the fastest-growing technologies in the world. The challenge in OLED fabrication, especially larger-area OLEDs, is its relatively high costs and complexity. The lamination method at a vacuum-free environment is an approach to simplify and reduce the cost of fabrication. This paper reports our latest progress on OLEDs fabricated using the said method and condition. The processing parameters were explored and optimized. Spin coating the emissive Layer (PFO) at 1300 rpm and the anode (TC-07-S) at 3000 rpm yield the best results in terms of current conduction and success rate. Laminating the OLEDs at 160 °C, with 245 N of force, and for 30 seconds, gave the best results in terms of previously stated parameters. Furthermore, the constituting materials of the OLEDs were explored. It was found that TC-07-S as an anode, PFO as the light-emitting material, a 30-micrometer thick aluminum foil as the cathode, and Kapton as the dielectric and adhesive material yielded the best results. These results may pave the way for other innovative methods to fabricate OLEDs with a simple and affordable processes.
Incubator Analyzer Function Test in Laboratory Scale: Temperature Uniformity, Relative Humidity, Noise Level and Airflow Handayani, Indah Nursyamsi; Mamurotun; Suharyati; Muthmainnah, Syamila Yasmin; Muhammad, Farhan
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.25

Abstract

A function test is conducted to assess the equipment's performance, component function, output, and safety. The aim of the incubator analyzer function test is to determine the performance parameters, including mattress temperature, ambient temperature, humidity, noise, and airflow. In this article, we present an analysis of a prototype incubator analyzer through a comparative test method against a standard incubator analyzer. The testing procedure adheres to SNI IEC 60601-2-19-2014, which outlines special requirements for the basic safety and essential performance of infant incubators. The incubator analyzer prototype was designed using specific components such as PT100 resistive temperature detector, single chip humidity sensor module SHT 11, airflow sensor, microphone amplifier MAX4466 as a sound sensor, and a human-machine interface Nextion display. The function test of the incubator analyzer was conducted at an authorized institution on a laboratory scale. The results indicate that the prototype achieves an accuracy of over 98% for temperature measurement and more than 94% for relative humidity at temperature settings of 32°C and 36 °C.
Implementation of Thermal Camera for Human Stress Detection: A Review Hendryani, Atika; Nurdinawati, Vita; Sambiono, Andy
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.28

Abstract

Stress has become a major problem that people face today. The high level of competition and environmental demands make people more susceptible to stress. Stress can interfere with a person's ability to work effectively. If left unchecked for a long time, stress can cause various dangerous diseases such as hypertension, heart problems, and others that can lead to death. Research has been conducted for a long time to detect stress. Various technologies have been used to detect and anticipate stress that occurs in humans. One promising technology for detecting stress is the use of thermal cameras. Thermal cameras have several advantages: being non-contact and non-invasive, quick, easy to use, and cost-effective. In general, the architecture of the stress detection system using a thermal camera consists of several stages, including image acquisition, pre-processing, ROI tracking and selection, feature extraction, and statistical analysis or classification. This paper aims to review the use of thermal cameras in detecting stress in humans. This paper also seeks to answer the research question of what analysis can be done to improve stress detection accuracy using thermal camera images. Research shows that ROI selection must be carefully considered to obtain good accuracy. Combining thermal images with other data can improve accuracy in stress detection. Machine learning in classification provides many benefits in recognizing patterns but is highly influenced by the number of datasets used.
An Implementation of a Single Board Computer as a Home Vital Sign Monitoring System Using a Raspberry-Pi Susana, Ernia; Handayani, Indah Nursyamsi; Komarudin, Agus
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.29

Abstract

Health monitoring and related technologies are a growing area of research. The embedded technology enables designing and manufacturing a single board computer (SBC) based vital signs monitoring system prototype. This study aims to develop and test an equipped with a monitor of vital signs, including electrocardiogram, heart rate (HR), respiratory rate (RR), and body temperature. The results showed that the prototype could work well, all parameters could be displayed on the 7" TFT touch screen, and the operation used Bahasa instructions. Qualitative test results show all the parts and the function is working correctly. Quantitative heart rate test results show an accuracy of 95% with a range of 30-200 bpm and 93% for the temperature parameter.
Advancing Network Infrastructure: Integrating VXLAN Technology with Automated Circuit Operations and NOS Configurations Efendi, Arfan; Husna, Diyanatul; Nugraha, I Gde Dharma
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 1 No. 2 (2023)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v1i2.30

Abstract

Enhancing network infrastructure is achieved through integrating VXLAN technology, Python-automated circuit operations, and Ansible-driven Network Operating System (NOS) configurations, complemented by GitHub for reliable configuration backups. VXLAN, a robust network virtualization protocol, addresses the challenges of managing extensive network segments. Python scripts facilitate the automated analysis, creation, and management of network circuits, significantly boosting efficiency and accuracy. Ansible, a powerful automation tool, is employed to streamline NOS configurations, ensuring consistency and reducing manual overhead in network settings. Concurrently, GitHub, working in tandem with crontab scheduling, offers a dependable platform for the automated, regular backup of configurations, thus enhancing network resilience and simplifying recovery processes. The collective implementation of VXLAN, Python, and Ansible automation, along with GitHub for configuration management, marks a notable advancement in operational efficiency, underscoring their importance as critical components in the modernization and security of network infrastructures.

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