cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
I Gde Dharma Nugraha
Contact Email
i.gde@ui.ac.id
Phone
+6281558805505
Journal Mail Official
ijecbe@ui.ac.id
Editorial Address
IJECBE Secretariat Electrical Engineering Department, Faculty of Engineering, Universitas Indonesia Kampus UI Depok, West Java, Indonesia 16424
Location
Kota depok,
Jawa barat
INDONESIA
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. 3 No. 1 (2025)" : 10 Documents clear
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.
Transforming Humanitarian Response with IoT in Conflict Zones: Field Insights, Ethical Frameworks, and Deployment Challenges Parmadi, Budi Dhaju; Ramli, Kalamullah
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.v3i1.112

Abstract

The integration of Internet of Things (IoT) solutions into the delivery of humanitarian aid can be potentially transformative in improving the effectiveness of operations, time management, and the coordination of logistics in conflict-affiliated areas. However, there are some critical challenges, which include poor infrastructure, limited and irregular network coverage, increased cyber security risk, and cultural issues. Despite the fact that most of the existing literature focuses on these issues separately, this thematic review is the first to offer an integrated review of the infrastructural, security, and ethical aspects of IoT implementation simultaneously. In particular, the review reveals new approaches; decentralized IoT architectures, blockchain-secured networks, AI-assisted data analysis, and alternative network architectures. Specifically, it focuses on ethical governance, of addressing technocolonial issues, fair data management, and design for communities. This paper provides original, practical contributions and recommendations for strategic implications that guide researchers, policymakers, and humanitarian practitioners to develop resilient, scalable, and ethically informed IoT deployments. The directions for future research are outlined to develop sustainable IoT practices, comprehensive governance frameworks, and multi-stakeholder collaborations to improve the resilience and ethical appropriateness of humanitarian aid operations.
Cybersecurity Of Work From Anywhere Model For Government : A Systematic Literature Review Asyrofi, Muhammad Fahreza; Nugraha, I Gde Dharma
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.v3i1.113

Abstract

Presidential Regulation No. 21 of 2023 grants Indonesian civil servants (ASN) location flexibility, creating cybersecurity challenges that institutions and authorities have yet to fully address. Existing frameworks such as ISO 27001 and NIST provide only general remote work guidelines, lacking specific recommendations for the Work From Anywhere (WFA) model. This gap poses significant risks to data security and government operations, particularly as cyber incidents reported by the National Cyber and Crypto Agency of Indonesia (BSSN) continue to rise. The 2023 Indonesian Cybersecurity Landscape report recorded 347 suspected cyber incidents, including data breaches and the exposure of over 1.6 million records on the darknet, affecting numerous stakeholders. This study employs a Systematic Literature Review (SLR) to identify cybersecurity threats associated with remote work and explore effective mitigation techniques. The findings reveal five primary threats classified into two categories: human-centric threats (social engineering attacks, insider threats, and human errors) and technology-centric threats (malware-based and network attacks). To address these threats, the study identifies four key best practice themes: Awareness and Education, Phishing Protection, Technical Countermeasures, and Management and Audit. These themes provide a structured approach to enhancing cybersecurity in WFA environments. The results of this study serve as valuable input for formulating policy and technical guidelines to implement WFA in government settings. Future research should explore supply chain security, integration of WFA with on-site operations, cultural factors in security compliance, and governance frameworks to enhance cybersecurity resilience in government WFA environments.
Modeling Liver Fibrosis Using hiPSC-Derived Liver Organoids: Methods and Applications Ksatrianto, Faris; Suhantoa, Deviana Lavender; Aziz, Rizal; Natadilandes, Reyhan; Qamarani, audina; Nurjamil, Aris Muhammad; Widowati, Wahyu
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.v3i1.117

Abstract

Liver fibrosis is a pathological state marked by the excessive buildup of extracellular matrix due to persistent liver damage. Despite the potential of traditional medicines, including antiviral medications and lifestyle adjustments, to decelerate fibrosis progression, a completely effective treatment is still lacking. This article examines the function of human-induced pluripotent stem cells (hiPSCs) in mimicking liver fibrosis. HiPSCs can differentiate into multiple liver cell types, such as hepatocytes, hepatic stellate cells, and endothelial cells, facilitating the reconstruction of liver microarchitecture in both two-dimensional cultures and three-dimensional organoids. These technologies offer critical insights into the pathophysiological underpinnings of the disease, facilitate the discovery of therapeutic targets, and aid in the development of innovative antifibrotic drugs. The use of hiPSCs not only enables novel methods for disease modeling but also presents intriguing opportunities for more targeted and effective regenerative therapy for liver fibrosis.

Page 1 of 1 | Total Record : 10

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 3 (2025) Vol. 3 No. 2 (2025) Vol. 3 No. 1 (2025) Vol. 2 No. 4 (2024) Vol. 2 No. 3 (2024) Vol. 2 No. 2 (2024) Vol. 2 No. 1 (2024) Vol. 1 No. 2 (2023) Vol. 1 No. 1 (2023)