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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 10 Documents
 1 
Search results for , issue "Vol. 2 No. 4 (2024)" : 10 Documents clear
Analysis of Transformer Oil Condition of Dissolved Gas Analysis (DGA) Testing Results with Modified Conventional Methods Budhihadi, Randy Purnawan; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.72

Abstract

Power transformers are one of the most widely used important components in the electric power system. Dissolved Gas Analysis (DGA) testing is used to diagnose transformer failures before more severe damage occurs by analyzing gas indicators dissolved in transformer oil through several methods, one of which uses conventional methods. However, based on most of the tests conducted by researchers, the detection accuracy of the conventional method is still quite low. Therefore, this research has the main objective of identifying the weaknesses of one of the conventional methods, namely the Rogers Ratio method. This research method uses modifications to the fault diagnosis flow chart which is then applied in the interpretation of DGA test results on power transformers in the case of the GSUT #1 20/11 kV Transformer of Manokwari Gas Engine Power Plant (GEPP). Based on the results of this research, the previous method cannot diagnose the fault (Undetermined) while after being modified it can diagnose the “Thermal Fault 150-200OC. When compared with other conventional methods that have been tested such as the interpretation of the Duval Triangle method, the results of diagnosing “Thermal Fault < 300OC”, it means that in general can be known that there has been a thermal disturbance in the internal transformer at temperatures below 300OC. Thus the results of the modified interpretation of the Rogers Ratio method are better than before so that it can be applied as an additional technique for interpreting DGA test data
Techno-Economic Optimization Study of Renewable Energy Planning in Buru Island Electricity System Z Day, Faizatul Hasanah; Samual, Muhammad Gillfran; Garniwa, Iwa; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.73

Abstract

One of the strategies to achieve Indonesia's NDC target in 2030 is through the development of renewable energy power plants, and the transition from fossil fuels to renewable energy. The use of diesel power plants, especially with the case on Buru Island as the only electricity supply, contributes to the production of emissions, and increases the Cost of Energy (CoE) of the utility system. On the other hand, Buru Island is rich in renewable energy potential, such as geothermal, hydropower, bioenergy, and solar energy. This study aims to design an optimal power generation system on Buru Island by considering the renewable energy mix, financial feasibility, reduction in the CoE of local electricity system, reduction in CO2 emissions, and the potential load growth of the local industry, i.e. fisheries industry sector. This study utilizes HOMER software to obtain a power generation scenario that can supply the load with the most optimal renewable energy penetration, the lowest Levelized CoE (LCOE), and the lowest CO2 emissions. Seven electrical systems on Buru Island were implemented to form 4 systems, namely an integrated system of 4 previously distributed systems, and 3 other distributed systems. The result of this research gives out the most optimum configuration of hybrid or complete renewable energy-based power plant configuration for each system. The configurations can reduce the CoE up to 20.17 cUSD/kWh, and up to zero CO2 emission.
Optimization of Heat Rate and Greenhouse Gas Emission Reduction at Coal-Fired Power Plants in Indonesia Through Machine Learning Modeling Setyawan, Ariandiky Eko; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.77

Abstract

This study aims to develop predictive models for the heat rate of coal-fired steam power plants (CFSPPs) in Indonesia using various machine learning techniques and to identify factors influencing greenhouse gas emissions, specifically CO2. Techniques used include Linear Regression, Lasso Regression, Polynomial Regression, Ridge Regression, Support Vector Regression, Random Forest Regression, Gradient Boosting Regression, Elastic Net Regression, AdaBoost Regression, Neural Network Regression, Decision Tree Regression, and Extra Trees Regression. The data consists of 468 performance test results from CFSPPs, covering operational parameters such as boiler type, ambient temperature, flue gas temperature, and unburned carbon. Analysis shows that the Extra Trees Regression model provides the best performance with an R-squared value of 0.947, MAE of 133.648, MSE of 34694.478, and RMSE of 186.265 for heat rate modeling, and an R-squared value of 0.993, MAE of 21.02, MSE of 1402.858, and RMSE of 37.455 for CO2 emissions modeling, demonstrating high accuracy and good generalization. Significant factors influencing the heat rate include Gross Power Output (GPO), Net Power Output (NPO), load percentage, boiler type, coal HHV, coal consumption, and operational duration. This model is implemented using the Postman application for real-time heat rate and CO2 emissions prediction, facilitating integration with CFSPP’s operational systems. The research results indicate that the application of machine learning can improve energy efficiency and reduce CO2 emissions, supporting Indonesia's Nationally Determined Contribution (NDC) targets. This study provides new insights into the application of machine learning in the power generation industry and offers recommendations for further implementation and research.
Design and Development of L-Shaped Rectangular Microstrip Patch Antenna With Slot for Wi-Fi 6E Applications Belo, Jacinto Cipriano Ximenes; Zulkifli , Fitri Yuli
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.83

Abstract

This research focuses on designing and developing an L-shaped rectangular microstrip patch antenna with a ground slot optimized for Wi-Fi 6E applications. The goal is to ensure optimal performance in radiation efficiency, sufficient bandwidth, and signal reliability across the 2.4 GHz, 5 GHz, and 6 GHz frequency bands. Wi-Fi 6E, based on the 802.11a, 802.11b, and 802.11ax standards, offers speeds up to 11 Mbps, 54 Mbps, and 9.6 Gbps, respectively, and provides more channels with wider bandwidth and minimal interference, enhancing applications such as 4K/8K streaming, online gaming, and IoT. The antenna's design is crucial to exploit Wi-Fi 6E's potential, delivering optimal radiation efficiency, bandwidth, and signal reception. Measurement results show that the antenna's bandwidth in the first band ranges from 1.150 to 3.091 GHz (1941 MHz) and in the second band from 3.688 to 7.243 GHz (3555 MHz). The radiation pattern remains omnidirectional, and total efficiency is 82.27% at 2.436 GHz, 90.42% at 5.3 GHz, and 91.95% at 6.56 GHz. The gain is 4.19 dB at 2.4 GHz, 4.87 dB at 5 GHz, and 5.23 dB at 6 GHz. These results align with the simulations, making the proposed antenna a suitable candidate for Wi-Fi applications.
High Gain Cascaded Two-Stage Low Noise Amplifier Design Using T-Matching Stub Resonator Rusdiyanto, Dian; Adhiyoga, Yohanes Galih; Mahendra, Adhi; Aji, Arie Pangesti
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.84

Abstract

The capability improvement of a telecommunications device influences the performance aspect of the device. In order to access the signal source, repeaters are required to reach signal to subscribers or improvements to the user's device itself that are able to reach signals that are far away. One of the telecommunications devices on the user side includes a low noise amplifier (LNA) which is useful for strengthening the signals and reducing the noise level received. This equipment supports to stay within reach of small signals received by users in areas with less signal. In this research, LNA is discussed specifically, especially increasing LNA gain and impedance matching methods through resonator modification. The LNA is designed using a two-stage cascading method which has high gain and low reflection factor. The simulation results show that the proposed LNA design succeeded in obtaining a gain of 31.6 dB where the stability and noise figure values obtained 3.79 dB and I.87 dB respectively. The T-Matching stub method used to adjust input and output impedance also succeeded in having a low reflection coefficient. At a frequency of 1575 MHz, the input and output reflection coefficients were obtained -70.5 dB and 56.2 dB. LNA design is divided into three steps, such as LNA bias design, single-stage LNA and cascaded connected two-stage LNA. The simulation results show that the LNA design meets the agreement with the desired target specifications.
An Analytical Review on Emerging Future Network Technologies in Healthcare: Issues, Challenges and Prospects for the Future Kumar, Narendar; Salman, Muhammad; Waqar, Abdul; Khan, Muhammad Salman
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.86

Abstract

This review research article covers in detail healthcare issues and problems related to emerging network technologies, including 5G, satellite communication, and body area networks. The main target is to provide readings that are preliminary so as to get a more rapid in-depth exploitation of this area through which the existing landscape can be figured out and related areas in which further research and probing can be done. This review focuses on core components such as security and privacy, transmission performance, regulatory issues, coexistence issues, and standardization, and explores the issues of securing patient data and confidentiality and measures of performance which play a critical role in healthcare application. Identifying key challenges and matured areas in the review, the review points out researchers to confirmed areas where more efforts are required, like robust security protocols, more innovative regulatory solutions, and new interoperability standards. In this way this study corroborates the need for continued innovation and research in healthcare network technologies that will ultimately result in an improved quality of healthcare delivery and, hence, an improved result for the patient
Comparative Analysis of LSTM and Bi-LSTM Models for Earthquake Occurrence Prediction in Tokai-Japan Region Hamdi, Azhari Haris Al; Nugroho, Hapsoro Agung; Kusumoputro, Benyamin
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.87

Abstract

This study compares the performance of Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models in predicting earthquake occurrences in the Tokai region, using data from the United States Geological Survey (USGS) dataset. Given the importance of accurate earthquake prediction, particularly in high-risk regions, this research focuses on assessing the effectiveness of each model in identifying occurrence and non-occurrence events. Both models were tuned to optimize sensitivity and specificity through adjustments in sequence length, learning rate, and additional hyperparameters, with results evaluated using metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). Findings reveal that while both models achieved high sensitivity, the LSTM model demonstrated superior specificity and AUC, indicating a more balanced performance in distinguishing between earthquake occurrences and non-occurrences. The results show that LSTM outperforms Bi-LSTM in terms its classification metrics. LSTM achieved an accuracy of 76%, compared to 55% for Bi-LSTM. For the AUC metric, LSTM scored 66%, while Bi-LSTM scored 67%.
Exploring the Potential of Electrospun Polymers for High-Performance Dental Composite: A Mini Review Sudiyasari, Nadiya; Rahman, Siti Fauziyah
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.91

Abstract

Dental resin composite is the most common material used in dentistry. Resin composite refers to combination of two or more materials that typically consists of matrix polymers, fillers, and a coupling agent. Fillers are essential in composites, as their presence significantly improves the material's hardness. However, beside its excellent mechanical properties, Resin composite also has several limitations, including polymerization shrinkage, a high coefficient of thermal expansion, and low wear resistance. Adding reinforcement materials such as electrospun fiber to composite fillers has shown improvement of its mechanical properties. Electrospun fiber refers to a fiber that produced through electrospinning methods. There are various types polymers used in electrospinning fabrication, such as Poly(methyl methacrylate) (PMMA), Polyacrylonitrile (PAN), Polyether ether ketone (PEEK), Polyvinyl alcohol (PVA), Polycaprolactone (PCL), and Polylactic acid (PLA). The electrospinning method utilizes a high-voltage electrical source applied to these polymer solution. Electrical voltage will initiate the formation of droplets that then elongate to form fibers. Electrospun fibers have versatile applications in dentistry and can be used as a reinforcing agent for dental composite restorations. Therefore, electrospun fibers has a lot of promising potential in dentistry, as they can produce materials with excellent mechanical properties by using a simple and efficient method.
Effect of Inverter Frequency on Electric Motor-Driven Air Conditioning Systems Repurposed from Combustion Engine Cars for Electric Vehicle Applications Kenny, Jonathan; Yusivar, Feri
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.93

Abstract

This research introduces a framework for the adaptation of conventional automotive air conditioning systems in electric vehicle conversions by modifying them to operate with an electric motor driven by an inverter, aimed at reducing waste and promoting energy efficiency. The experiment was conducted on an AC system from a combustion engine car, tested independently to evaluate the effects of varying inverter frequencies on cooling performance and power consumption. Data were collected using multiple blower fan speeds, with additional alternator integration tested to maintain system efficiency. The results highlight optimal settings for minimizing energy consumption while achieving effective cooling, providing valuable insights for sustainable EV conversions.
Feature Importance in Predicting Generator Rotor Thermal Sensitivity: A Random Forest-LSTM Approach Wardhana, Aryatama Wisnu; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 2 No. 4 (2024)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v2i4.90

Abstract

Thermal sensitivity incidents on generator rotors at Muara Tawar Power Plant have increased over the past five years, which will have an impact on the overall performance of the power plant. The general method of conducting thermal sensitivity testing requires the generating unit to be in a certain operating pattern, thus limiting the analysis of anticipating events in real time. Correlation analysis between excitation current variables, reactive power, vibration, and temperature needs to be carried out periodically. The acquisition of these operating parameters was carried out on three generator rotors for 14 days per minute and will be implemented into a machine learning model. This study uses the Random Forest model to predict vibrations on the rotor and determine featureimportance values, with the addition of Long Short-Term Memory (LSTM) modeling to predict future trends based on feature importance. The results show that the Random Forest model can predict vibrations in the rotor and determining the importance of the features used, with an average evaluation metric RMSE of 0.92% and R2 of 81.62% on the exciter side, and RMSE of 2.75% and R2 of 61.42% on the turbine side. The LSTM model also demonstrates good capability in predicting future trends in thermal sensitivity identification based on exciter current features with an RMSE of 7.29% and for reactive power features of 6.52%, indicating that the proposed modeling implementation allows a better understanding of the variables relevant to thermal sensitivity, thus predicting them in the future can produce comprehensive operation and maintenance strategies.

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