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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 93 Documents
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Optimization of Preventive Maintenance Planning for the Motor Cooling System at PLTGU Using Differential Evolution Putranugraha, Derry; Garniwa, Iwa
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 3 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i3.132

Abstract

Determination of the optimal preventive maintenance time of the three-phase induction motor (88WC) during operation at 380V in the cooling system of the Semarang Gas and Steam Power Plant (PLTGU) is done by combining the Power-Law Non-Homogeneous Poisson Process (NHPP) model and the Differential Evolution (DE) Algorithm to achieve minimum total maintenance cost. The parameters of NHPP, β = 1.75 and η = 7,198.99 hours, are estimated using the least squares method from the historical failure data for the 2020–2024 period, recording failures beyond 20,000 operating hours. The DE optimization results provide the optimum PM time of 371.60 hours to reduce the total cost from IDR 28,198,935 (for the 500-hour interval) to IDR 20,299,822, achieving a cost savings of 38%. Validation is performed using Monte Carlo simulations with 1,000,000 iterations that yield a pre-optimization failure probability of 0.56%. Sensitivity analysis using a ±20% parameter variation also proves the model's robustness. This data-driven framework is thus anticipated to increase the reliability and cost-effectiveness of the PLTGU cooling system and is scalable to other power-generating facilities
Defying Data Scarcity: High-Performance Indonesian Short Answer Grading via Reasoning-Guided Language Model Fine-Tuning Faza, Muhammad Naufal; Purnamasari, Prima Dewi; Ratna, Anak Agung Putri
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 3 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i3.148

Abstract

Automated Short Answer Grading (ASAG) is crucial for scalable feedback, but applying it to low-resource languages like Indonesian is challenging. Modern Large Language Models (LLMs) severely overfit small, specialized educational datasets, limiting utility. This study compares nine traditional machine learning models against two fine-tuning strategies for Gemma-3-1b-it on an expanded Indonesian ASAG dataset (n=220): (a) standard fine-tuning predicting only scores, and (b) a proposed reasoning-guided approach where the model first generates a score rationale using knowledge distillation before predicting the score. The reasoning-guided model (Gemma-3-1b-ASAG-ID-Reasoning) achieved state-of-the-art performance (QWK 0.7791; Spearman’s 0.8276), significantly surpassing the best traditional model in this study (SVR, QWK 0.6952). This work advances foundational LSA-based approaches for this task by introducing a more robust methodology and evaluation framework. Crucially, standard fine-tuning (Gemma-3-1b-ASAG-ID) suffered catastrophic overfitting (QWK 0.7279), indicated by near-perfect training but poor test scores. While the reasoning-guided LLM showed superior accuracy, it required over 35 times more inference time. Results demonstrate that distilled reasoning acts as a powerful regularizer, compelling the LLM to learn underlying grading logic rather than memorizing pairs, establishing a viable method for high-performance ASAG in data-scarce environments despite computational trade-offs.
Reliability Improvement of Defense Scheme Implementation Using Adaptive Load Shedding Based On System Strength Index Widyantara, Dwitiya Bagus; Garniwa, Iwa; Jufri, Fauzan Hanif
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 3 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i3.150

Abstract

One of the defense schemes in power systems is Under Frequency Load Shedding (UFLS), designed to mitigate cascading blackouts caused by frequency disturbances. UFLS operates based on predetermined frequency thresholds and time delays, which inherently characterizes it as a static protection mechanism and may cause unnecessary excessive or insufficient load shedding. Therefore, an Adaptive Load Shedding (ALS) approach started to gain popularity, which enables load shedding based on real-time conditions, particularly during generator outages. In this research, a comparative analysis is conducted between the conventional UFLS method and a newly developed ALS scheme that integrates the System Strength Index (SSI) to improve the system's reliability, as evaluated by Energy Not Served (ENS). The proposed ALS algorithm processes real-time feeder load data, ranks the feeders by load magnitude in descending order, and optimizes the load shedding setpoints by incorporating the SSI. The proposed method is simulated in the Flores power system model using actual historical data for two load conditions: the highest and the lowest. The results show that the proposed method outperforms the conventional UFLS by 7.31% in terms of improved ENS.
Pitch Control of Variable Speed Wind Turbine with Permanent Magnet Synchronous Generator (PMSG) Using Single-Neuron PI Controller Rohadatul ‘Aisya; Faiz Husnayain
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 4 No. 1 (2026)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v4i1.165

Abstract

Variable-speed wind turbines with Permanent Magnet Synchronous Generator (PMSG) offer high efficiency in converting wind energy into electrical power. However, under wind conditions exceeding the rated speed, a pitch angle control mechanism is required to maintain output power stability and protect the system from overloading. Pitch angle control is commonly implemented using conventional PI controllers. This study aims to implement a single-neuron PI controller as an adaptive solution for regulating the pitch angle in a PMSG-based wind turbine system. The proposed control method combines the basic structure of a PI controller with adaptive learning based on a single neuron, enabling real-time adjustment of the controller weights based on output power error. The simulation and modeling of the wind energy conversion system are carried out using MATLAB/Simulink, including the modeling of the wind turbine, PMSG, conventional PI-based pitch control, and single-neuron PI-based pitch control. Simulation results demonstrate that the single-neuron PI controller effectively maintains the output power close to the reference value, with minimal deviation and high stability across various wind speeds exceeding the rated condition. Compared to the conventional PI controller, this method offers superior adaptability to system parameter variations and dynamic wind speed changes.
The Efficiency of Voltage Stabilizer Installation in Addressing Power Quality Issues through Analysis Using an Injection Power Analyzer in the Manufacturing Industry of PT XYZ Varin Pasaribu; Faiz Husnayain
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 4 No. 1 (2026)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v4i1.173

Abstract

In today's modern era, electrical energy has become a vital secondary need for society and serves as a key foundation for economic development. Along with the rapid advancement of complex electronic devices, various power quality disturbances have emerged, negatively impacting industrial equipment such as active line modules and motor drivers, potentially disrupting production processes. A similar issue occurred at PT XYZ, prompting real-time power quality monitoring using a power analyzer installed on the LVMDB panel. This device can record and analyze power quality trends visually. Based on the data, PT XYZ took corrective actions, including adjusting the tap changer position from 3 to 1 and installing a voltage stabilizer, which effectively stabilized the voltage supplied to production machines. Furthermore, a harmonic analysis simulation showed a Total Harmonic Distortion of 8,35% for current and 7,92% for voltage. After applying a power filter design in ETAP, these values significantly decreased to 1.98% (current) and 2.08% (voltage). Based on these findings, it is recommended that PT XYZ proceed with the installation of a power filter to comprehensively improve power quality.
Development of Prompting Techniques to Trigger Proactive Dialogues in Large Language Models Muhammad Firdaus Syawaludin Lubis; Cecilia Inez Reva Manurung Manurung
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 4 No. 1 (2026)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v4i1.187

Abstract

The rise of Artificial Intelligence (AI) challenges the integrity of traditional educational assessments, positioning the oral examination as a robust method for verifying authentic understanding. However, its large-scale implementation is hindered by significant logistical challenges, including time, resources, and evaluation consistency. This research addresses a foundational limitation for using Large Language Models (LLMs) in automated oral exams: their inherent passivity and failure to handle ambiguity, which are critical for effective assessment. To address this critical gap, we established an experimental framework and created two specialized datasets to systematically evaluate prompting techniques designed to elicit proactivity. Using the GPT-4o model, our comparative analysis reveals that the optimal strategy is highly task-dependent: for ambiguity detection (CNP), a Proactive Chain-of-Thought (PCoT) zero-shot approach achieved a near-perfect 0.99 F1-Score; for generating clarification questions (CQG), the PCoT few-shot variant was most effective; and in target-guided scenarios, a simpler proactive prompt proved superior. These findings provide foundational insights for developing automated oral examiners capable of nuanced, human-like dialogue, thereby addressing the scalability issues of traditional assessment methods.
Evaluation Of the Legality and Requirements of Starlink in Indonesia Using a Regulatory Impact Analysis Approach Ficco Clivano Ikhsan; Gunawan Wibisono
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 4 No. 1 (2026)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v4i1.210

Abstract

Indonesia has a territory and large population. Indonesia is one of the largest countries in the world, with an internet penetration rate of around 79% of the total population as of 2024. This has prompted Indonesia to improve its network and other telecommunications-related issues. This study evaluates the feasibility of utilizing Starlink satellites to equalize telecommunications access in Indonesia. Based on Law No. 36 of 1999 concerning Telecommunications, which mandates the provision of services to all regions, this study aims to evaluate the role of Starlink while identifying legal and regulatory obstacles related to this new technology. It also analyzes the legality of this technology and explains the sovereignty issues raised by Starlink. By applying the Regulatory Impact Analysis method, which uses the main stages of problem definition, policy identification, impact and policy evaluation, stakeholder consultation, and selection of alternative policies through interviews and framework. It began with the first option, which was a policy where the government permanently revokes Starlink's operational licenses to prioritize national sovereignty and data security. Option 2 approaches the issue with a temporary suspension and a comprehensive technical audit of the Network Operation Center (NOC) and Lawful Interception capabilities, and option 3 focuses more on giving Starlink more time to fully comply with regulations. Option 2 is the best choice because of its highest PMI score and its ability to balance the interests of regulators, operators, and the public. However, it temporarily disrupts service access for potential users, particularly in remote regions.
Design and Experimental Validation of an SDR-Based Private LTE Network for Flood Monitoring Bambang Candra Wibawa; Gunawan Wibisono
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 4 No. 1 (2026)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v4i1.213

Abstract

The widespread impact of hydrometeorological disasters, such as floods, necessitated a reliable and rapidly deployable communication infrastructure to support real-time monitoring and early warning systems. During such events, conventional public cellular networks frequently collapsed, hindering critical information exchange. To address this vulnerability, the study presented the design and performance validation of a private LTE network based on software-defined radio SDR USRP B210 and open-source software, intended as a resilient backbone for an IoT-based flood monitoring system. The system was tested to assess its signal quality and user experience metrics across varying radial distances. Signal analysis confirmed severe channel degradation, with the SINR dropping from the “Excellent” category at 3 m to the “Fair” and “Poor” category at 9 m. This SINR decline directly caused the downlink throughput to fall drastically from 27 Mbps to a low of 2.8 Mbps and significantly inflated the maximum latency to 87 ms due to frequent HARQ retransmissions. Despite these constraints, the network supported low-bandwidth IoT RSRP logging for modem consistency verification and sustained high-demand services including real-time data communication and voice and video calls. Overall, the results validated the system’s ability to establish resilient and self-sustained connectivity to support disaster mitigation and response operations.
Experimental Characterization of a Tri-Axial FBG Accelerometer for Underwater Thruster-Induced Vibration Detection Muhammad Alif Rahman Sukapraja; Retno Wigajatri Purnamaningsih; Sasono Rahardjo; Maristya Rahmadiansyah; Tinova Pramudya
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 4 No. 1 (2026)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v4i1.226

Abstract

This paper presents an experimental engineering validation of a tri-axial Fiber Bragg Grating (FBG) accelerometer for detecting thruster-induced vibrations in submerged environments. The sensor integrates three orthogonally arranged FBG elements within a compact, waterproof housing, enabling vibration measurements along three spatial directions. Experiments were conducted using a DC thruster as a repeatable vibration source under both free-air and underwater conditions. Comparative results show a marked increase in Bragg wavelength shift (ΔλB) when submerged, with peak-to-peak variations reaching approximately 0.0267 nm on non-primary axes and up to 0.1005 nm on the axis aligned with the excitation, compared to approximately 0.0044 nm in free air. Sensor sensitivity, repeatability, and hysteresis were evaluated through cyclic loading-unloading tests. The maximum hysteresis observed was 0.0053 nm, corresponding to approximately 5% of the full-scale wavelength shift, indicating stable and predominantly elastic behavior.
Energy Consumption Optimization for Flexible Job-Shop Scheduling in Manufacturing Industry Using Multi-Agent Reinforcement Learning Satwika Bintang Bahana; Naufan Raharya
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 4 No. 1 (2026)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v4i1.227

Abstract

Modern manufacturing industries are increasingly demanding production systems that are both flexible and efficient in handling dynamic operational conditions. Traditional scheduling approaches, such as the job-shop scheduling Problem (JSSP), are limited by the constraint that each operation must be executed on a predefined machine. To address this limitation, the flexible job-shop scheduling Problem (FJSSP) was introduced, allowing alternative machine options for each operation and thereby enhancing system flexibility. This study proposes a scheduling optimization approach based on Multi-Agent Reinforcement Learning (MARL), to support decision-making in complex production environments. Experimental results demonstrate that the proposed method reduces energy consumption by up to 40.62% compared to the First Come First Serve (FCFS) method and by 35.23% compared to the Fastest Available Agent (FAA) method. Moreover, the model shows superior performance in controlling theworst-case makespan and achieves significantly higher success rates in satisfying various production constraints compared to all tested rule-based methods.

Page 9 of 10 | Total Record : 93

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