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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 83 Documents
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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.
Economic Optimality of Automatic Generation Control in a Multi-Source Power System Using an Optimization Problem Approach Tambun, Laura Agnes; Fitri, Ismi Rosyiana
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 1 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i1.79

Abstract

Due to the incorporation of a high penetration of renewable energy resources into power generation, recent power system control strategies have combined economic dispatch (ED) and automatic generation control (AGC) to achieve economic operation. To this end, AGC parameters and control laws have been designed to optimize operation through the use of optimization approaches. Although existing studies indicate that the proposed AGC optimal control strategy offers superior performance compared to traditional AGC, the models used in these theoretical frameworks are typically dominated by a single energy source, such as a steam-turbine generator. Additionally, the models in existing studies do not consider the ramp generation constraints present in practical implementations. In this paper, we propose an algorithm to obtain the optimal AGC parameters to consider a more realistic power system with diverse sources. Numerical simulations are used to demonstrate the effectiveness of the proposed method.
Specification Design and Techno-Economic Analysis of Green Distribution Transformers with Amorphous Iron Cores and Natural Ester Oil for Sustainable Power Systems Kusumadinata, Angga; Dalimi, Rinaldy
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 1 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i1.88

Abstract

The initiatives for renewables and energy efficiency necessitates upgrading the design of distribution transformers, which still rely on petroleum-based mineral oil and contribute significantly to network losses. This research focuses on the design, development, and testing of a novel green distribution transformer. Green distribution transformers are defined as transformers that utilize environmentally friendly natural ester insulation oil and high-energy-efficiency amorphous iron cores. The design of the transformer is determined based on key characteristics and appropriate technical specifications and construction requirements, including the setting of new, very low no-load loss and load loss limit values. The prototype was developed and rigorously tested to assess its compliance with technical standards and evaluate its performance. The results demonstrate that the green distribution transformer meets the required specifications and exhibits significantly lower losses. A comprehensive economic analysis using total cost of ownership, considering the initial cost and operating costs, reveals that the green distribution transformer offers a lower total cost of ownership over its lifetime compared to conventional transformers. These findings highlight the potential of green distribution transformers to contribute to a more sustainable and efficient power grid.
Grid Import Optimization with Adaptive Deep Reinforcement Learning for PV-Battery Systems Karim, Romi Naufal; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 1 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i1.98

Abstract

This article explores the application of Deep Reinforcement Learning (Deep RL) to optimize energy management in photovoltaic (PV) and battery systems. The new framework presented here includes important innovations such as Rule-Based Action Smoothing for system performance consistency, PPO Multi-House Training to generalize across a wide range of energy usage patterns, and Post-Controller Integration to deal with real-time operational issues. While the dataset originates from Ireland, the model is adapted to align with Indonesia's dual-tariff system and local energy regulations. Simulation results demonstrate substantial cost savings, with reductions of up to 85.28% in stable scenarios and 18.26% in high-variability environments. These results highlight the flexibility and resilience of the methodology for using renewable energy to reduce costs and increase system efficiency. The model is, therefore, scalable for the implementation of intelligent energy systems in the residential context to support Indonesia's renewable energy goals and demonstrate its applicability to a broad range of scenarios.
Comparative Analysis of the Accuracy of Lithium-Ion Battery State of Charge Estimation Using Open Circuit Voltage-State of Charge and Coulomb Counting Methods with Simulink MATLAB Raihan, Sultan; Husnayain, Faiz
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 1 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i1.99

Abstract

This study investigates the State of Charge (SOC) estimation of a battery using secondary data from the Samsung INR 18650-20R (2000mAh). The methods employed include the OCV-SOC, Coulomb Counting, and the 1RC equivalent battery model at temperatures of 0°C, 25°C, and 45°C. This research evaluates the accuracy of these methods while assessing the influence of temperature on SOC estimation performance, which is critical for battery management systems in various applications. The equivalent battery model was tested using a 1A current with 10% SOC intervals, while the SOC estimation was performed under a 0.1A current during discharge conditions. The results indicate that the 1RC model demonstrates the smallest error at 25°C and 45°C, establishing itself as the most consistent method for SOC estimation across these temperatures. The Coulomb Counting method exhibits superior performance, with an R² value nearing 1 across all tested temperatures, showcasing its reliability in accurately reflecting SOC. Conversely, the OCV-SOC method delivers an R² range of 0.9757–0.9864, with its best accuracy observed at 45°C but significantly lower accuracy at 25°C, especially at low SOC levels (0–10%). The Coulomb Counting method’s high accuracy is influenced by its reliance on ideal simulation data, which excludes real-world challenges such as current leakage and sensor fluctuations. Nonetheless, the combination of the 1RC model and the Coulomb Counting method proves more reliable for SOC estimation under diverse temperature conditions compared to the OCV-SOC method.
RTBTS: A Real-Time Behavioural Training System to Mitigate Psychological Vulnerabilities in Social Engineering Attacks Kumar, Narendar; Muhammad Salman
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 1 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i1.103

Abstract

The aim of this research is to identify the psychological traits that make people susceptible to social engineering attacks and the effectiveness of current cybersecurity training. The study tries to identify how these factors can be better utilized to enhance the resilience of individuals in response to such an attack, due to a psychological or training deficiency. This involves data collection through structured surveying on internet platforms such as Google Forms. The analysis has been done by means of Python using statistical techniques, focusing on the descriptive analysis and regression analyses that set the links of psychological features and sensitivity to social engineering influenced by training programs. It followed from the research that certain psychological features of a person, like a high level of trust without its verification and readiness to conform with authority, raise his or her susceptibility to social engineering essentially. The training programs assessment had shown positive attitude to their helpfulness though deficiencies in adaptability and frequency of trainings reduce its potential to neutralize sophisticated social engineering techniques. These results reflect that, although the existing training is fairly successful, there is an urgent need for more flexible training methods that would consider individual psychological profiles and be updated regularly in combat with emerging social engineering strategies. Guided by these considerations above, this research supports the establishment of a Real-Time Behavioural Training System, RTBTS, continuous monitoring of dangers for dynamic adapted training modules.
Cyber Kill Chain Framework Approach to Map Potential Attack Vectors on Windows-based OS Syifa, Amanda Fairuz; Salman, Muhammad
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 1 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i1.107

Abstract

The widespread adoption of Windows 11 necessitates a comprehensive evaluation of its security vulnerabilities, particularly in light of increasingly sophisticated cyberattacks. This study exclusively focuses on Windows 11 Home and Enterprise editions, applying the Cyber Kill Chain framework to map potential attack vectors. The analysis reveals significant weaknesses in SMB and RDP protocols, with Windows 11 Enterprise proving more vulnerable to specific threats such as SMB Relay Attacks. Adversary emulation using the Caldera platform successfully simulated real-world cyber threats, highlighting critical security issues, including the extraction of sensitive information and privilege escalation risks through PowerShell. The emulation demonstrated that commands could identify user accounts and shared directories, exposing potential avenues for unauthorized access. Recommended countermeasures include enabling SMB signing, enforcing strong password policies, disabling unused RDP services, and deploying active antivirus solutions. This research provides key insights into enhancing the security posture of Windows 11 against modern cyber threats, emphasizing the importance of proactive security measures and continuous vulnerability assessments.
Day-Ahead Solar Power Forecasting Using a Hybrid Model Combining Regression and Physical Model Chain Pongmasakke, Erwin Pauang; Liu, Jian-Hong; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 1 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i1.108

Abstract

Solar power forecasting is essential for integrating PV plants into power grids, ensuring stability and aiding system operators (SOs) in decision-making. However, existing day-ahead models struggle with rapid weather changes, while deep learning models require extensive historical data, making them impractical for new PV plants.This study proposes a hybrid approach combining the XGBoost algorithm for hourly solar irradiance prediction using Numerical Weather Prediction (NWP) data and a physical model to convert irradiance into power. The XGBoost model is periodically retrained via a sliding window mechanism to adapt to dynamic weather conditions.A case study using two years of 271 kWp PV data from NIST (US) and historical NWP data from ECMWF ENS for GHI forecasting, alongside ECMWF HRES for power conversion, demonstrated the method’s effectiveness. Using just one week of historical data for initial training, the model achieved an nRMSE of 13.35%–13.53%, nMAE of 6.9%–7.03%, and nMBE of -2.03% to -0.29%. The proposed approach improves PV forecasting reliability for new plants with limited data, serving as an intermediary solution until sufficient historical data is available for deep learning models.

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