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I Gde Dharma Nugraha
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IJECBE Secretariat Electrical Engineering Department, Faculty of Engineering, Universitas Indonesia Kampus UI Depok, West Java, Indonesia 16424
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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. 3 No. 2 (2025)" : 10 Documents clear
Artificial Intelligence Risk Identification: Challenges, Impacts, and Mitigation Strategies Syukrina, Ulfia; Nugraha, I Gde Dharma
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.109

Abstract

Artificial Intelligence (AI) has rapidly transformed various industries, providing significant benefits in automation, decision-making, and efficiency. However, AI also presents numerous risks, including bias, lack of transparency, security vulnerabilities, and regulatory challenges. This study employs a Systematic Literature Review (SLR) approach to identify and categorize key risks associated with AI implementation. The findings indicate that AI risks can be classified into technological, social, and regulatory aspects, each posing unique challenges. Algorithmic bias, privacy concerns, and the lack of global AI governance frameworks highlight the need for more robust risk mitigation strategies. To address these challenges, this study recommends enhancing fairness-aware AI models, strengthening AI governance, and increasing public AI literacy. Future research should focus on improving AI accountability, security measures, and ethical guidelines to ensure responsible AI adoption.
Optimizing Generation Costs in Electricity Supply Business Plan for Electricity Companies in Indonesia: A Reliability-Based Approach for the Sumatra Power System Sikumbang, Supriyanto; Garniwa, Iwa
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.111

Abstract

Ensuring a stable and sustainable electricity supply requires effective planning that balances cost efficiency and system reliability. This study explores the optimization of Basic Generation Cost (BPP) in PT PLN (Persero)'s Electricity Supply Business Plan (RUPTL) 2025-2034 while considering the reliability of the Sumatra power system. Using Digsilent PowerFactory, simulations incorporating Unit Commitment and Economic Dispatch methodologies were conducted to achieve cost reductions without compromising system stability. The significant result of this theses is optimization with economic dispatch reduces BPP up to 41,4% compared to conventional methods, enhancing power system cost efficiency. Increasing voltage reliability from 0.99 p.u. in 2025 to 1.01 p.u. in 2034. Higher renewable energy integration in 2034 reduces fuel costs but increases challenges in maintaining frequency and voltage stability. Strategic recommendations include increasing transmission capacity, implementing energy storage systems, and optimizing unit commitment to balance cost and reliability. This research offers valuable insights for power system planning, addressing energy transition challenges and facilitating the integration of renewable energy sources in Sumatra. Keywords: Basic Generation Cost, System Reliability, Economic Dispatch, Digsilent PowerFactory
Development Of Detection Model For Skin Diseases In Pets Using Image Processing And Deep Learning Techniques Taufiqoh, Salma Dewi; Purnamasari, Prima Dewi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.114

Abstract

Early detection of skin diseases in pets is essential but often hindered by the cost and complexity of clinical diagnosis. This study introduces a deep learning–based system for identifying three common pet skin diseases—Ringworm, Scabies, and Earmite—using images captured with mobile phone cameras. The system integrates classical image preprocessing techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and Hue-Saturation-Value (HSV) segmentation, with a custom convolutional neural network (CNN) designed for disease-specific classification tasks. Two separate models were developed: a multi-class CNN model for classifying Ringworm, Scabies, and Undetected conditions, which achieved a test accuracy of 83%, and a binary CNN model for classifying Earmite versus Undetected, which achieved 100% accuracy, precision, and recall on both test and unseen validation sets. Compared to transfer learning models such as ResNet-50 and VGG16, the proposed CNN models demonstrated superior performance under limited-data conditions (72 images total), emphasizing the advantage of domain-specific model design and preprocessing. These findings suggest that disease-adapted CNN architectures, combined with targeted preprocessing, can support accurate and accessible veterinary screening using mobile devices. Future work will focus on expanding the dataset and deploying the model in a real-time mobile diagnostic application.
Optimizing Power Transformer Failure Identification: A Multi-Method Framework Based on Normalized Energy Intensity According to IEEE C57.104-2019 Standards Adapted to Indonesian Power Transformer Characteristics Kurniawan, Wahyu Citra; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.121

Abstract

This research develops and validates a multi-method diagnostic framework by integrating Normalized Energy Intensity (NEI) parameters according to IEEE C57.104-2019 standards adapted for Indonesian power transformer populations. Analysis of 1525 DGA samples from PLN Indonesia transformers reveals significant differences in percentile thresholds compared to North American standards. Using unadapted North American thresholds categorized 68.4% of transformers as critical (DGA Status 3), while adapted thresholds reduced this to 25.1%. Duval Triangle 1 identified Discharge of Low Energy (D1) as the dominant failure type (35.4%), while Duval Pentagon 1 showed dominance of Discharge of High Energy (D2) (39.4%), and Duval Pentagon 2 identified Stray gassing (S) (27.6%) and Overheating without paper carbonization (O) (22.3%). Pearson correlation analysis on transformers with O₂/N₂ ratio ≤ 0.2 showed strong correlations between NEI Oil with ethylene (R = 0.877) and methane (R = 0.845), while NEI Paper strongly correlated with carbon monoxide (R = 0.934). NEI Oil combined with hydrocarbon gas concentrations provided more consistent patterns with multi-method fault identification than NEI Paper. Multi-method validation proved absolute gas concentration methods more reliable than gas ratio methods. This framework improves maintenance efficiency by reducing false alarms and optimizing preventive strategies.
Lightning Performance Design Review of 150kV Overhead Transmission Line Hakim, Fakhri; Fitri, Ismi Rosyiana; Widyanto, Aji Nur
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.122

Abstract

Lightning overvoltage constitutes the predominant cause of transmission line interruptions in Indonesia, significantly compromising system safety and reliability. This paper presents comprehensive simulations of lightning performance on 150kV overhead transmission lines using ATP-EMTP software, with particular focus on evaluating shielding failure and back flashover occurrences on one of the standard tower designs used by PT PLN (Persero). The transmission line model incorporates shielding wires, phase conductors, tower surge impedance, current-dependent footing resistance behavior, and arcing horns. Simulations were conducted to investigate three key aspects: maximum shielding failure current based on various Electrogeometric Model (EGM) constants, the impact of footing resistance on critical flashover current, and the effect of arcing horn length variations on critical flashover current. The analysis also accounts for phase angle variations in system voltage. Results highlight the significant influence of these variables on the Lightning Flashover Rate (LFR) of existing tower designs. Increasing footing resistance from 10Ω to 20Ω elevates Back Flashover Rate (BFOR) by 16.39%, while further increases to 30Ω and 40Ω yield only marginal increases of 17.33% and 17.76%, respectively. Notably, arcing horn gap length modifications demonstrate substantial performance improvements, with 1.4m and 1.5m gaps reducing LFR by 17.37% and 30.87%, respectively, compared to the 1.3m reference configuration. Analysis of maximum shielding-failure currents across varying EGM coefficient sets indicates that shield wires fail to intercept currents in the range of 2.53 kA to 52.81 kA.
Power Quality Improvement for Voltage Sag Issue in Industrial Customers Putra, Muhamad Mandala; Sudiarto, Budi
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.131

Abstract

Customer demand today is no longer limited to ensuring the reliability of electricity supply, but also includes the continuity and delivery of high-quality electrical power. One of the main challenges affecting power quality is voltage sag, a condition frequently experienced by industrial customers, particularly at PT. Samator Gas Industri Palembang. This study aims to analyze the causes of voltage sag problems and evaluate the effectiveness of technical solutions. The methodology involves analyzing field observation data, recordings from a Fluke Power Quality Meter (PQM), and simulations of Line-to-Line (LL) and Three-Phase (L-3P) short-circuit faults using ETAP software. The simulation results are evaluated using the ITIC Curve to determine whether the observed voltage levels fall within acceptable operational boundaries or enter the prohibited zone. Simulations were conducted under normal operating conditions by integrating three technical solutions: Static VAR Compensator (SVC), IS-Limiter, and Diesel Rotary UPS (DRUPS). The findings indicate that although SVC can accelerate voltage recovery after a disturbance, its effectiveness is lower compared to the others. The IS-Limiter provides a rapid response to limit fault current and prevent the propagation of disturbances throughout the system. Meanwhile, DRUPS offers the fastest and most reliable voltage recovery, restoring voltage to 100% in less than 20 milliseconds.
Analysis of Additional Generation Planning in the Batam-Bintan Power System to Improve Reliability Purba, Kevin Pangestu; Garniwa, Iwa
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.133

Abstract

The Batam-Bintan electrical system encounters operational challenges due to inadequate new power plants being commissioned to meet the increasing demand. Bintan Island's supply dependency on Batam Island through the undersea cables and 150 kV SUTT places operational stress systemically and adds vulnerability to disruption. The focus of the research is to optimize the system reliability through peak load forecasting up to 2030 and refining the strategic locations and sizes for the new power plants. The calculation forecast employs a second-order polynomial regression method, whereas the load flow analysis is performed with DIgSILENT PowerFactory 2022 software. Based on the research, the peak load is expected to grow from 675.2 MW in 2024 to 1,322.1 MW by 2030. To attain reliability, 940 MW of additional generation capacity is required, which is made up of 580 MW of DG (distributed generation) and 360 MW of central generation. The placement of DG is focused on substations that are overloaded or approaching overload, while centralized generation is positioned where power loss is lowest. The evaluation results indicate the additional generation makes it possible to maintain voltage stability, reduce dependence on PLTU XYZ and meet the reserve power requirement of a 35% power margin.
Illicit Cryotocurrency Investigation Digital Forensic Framework: Integrating Off-chain and On-Chain Analysis for Two Types of Crime Regina, Oliva; Ramli, Kalamullah; Amarullah, Abdul Hanief
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.135

Abstract

Cryptocurrencies have emerged as integral components of modern financial ecosystems, yet their pseudonymous nature poses significant challenges for digital crime investigations. This study proposes the Illicit Cryptocurrency Investigation Digital Forensic Framework, a novel model that integrates both on-chain and off-chain forensic techniques into a cohesive investigative process. Unlike prior research that treats blockchain analysis and conventional digital forensics separately, this framework combines blockchain transparency with contextual digital artifacts to form a unified approach. Validated by expert judgement from digital forensic practitioners, the framework is designed to address two primary crime scenarios: Type A, where investigations begin with suspect-owned devices; and Type B, where blockchain transactions provide the first investigative lead. The framework’s structured process—from identification to reporting—ensures evidentiary integrity, traceability, and legal admissibility. Beyond its practical application, the study lays groundwork for future developments, including the integration of artificial intelligence and cross-border legal interoperability in cryptocurrency-related crime investigations
Development of Disturbance Type Detection Using Convolution Neural Network for Fault Signature Analysis Putra, Kharisma Darmawan; Garniwa, Iwa; Jufri, Fauzan Hanif; Oh, Seongmun
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.136

Abstract

The development of technology in electrical systems is growing rapidly, increasing power system complexity, which causes the operation and maintenance of the power system networks to become more complicated, especially when a disturbance occurs in the networks. To overcome the issue, there is a need to utilize the tools available as much as possible to manage the power system networks. Nowadays, the power system network is equipped with protection relays and controls that provide various data about the systems, such as the Disturbance Fault Recorder (DFR), which monitors and records the system’s characteristics during network disturbance events. DFR holds information on the system’s parameters during a fault, but it cannot recognize the type or cause of the disturbance. Hence, this paper proposes a method based on the Convolution Neural Network (CNN) model to analyze the DFR’s data and determine the type/cause of disturbance so it can be used to manage the follow-up actions properly. Based on the research results, CNN, with six types of disturbance classification, has an accuracy of 93,87%. Based on the results obtained, the accuracy of CNN using the VGG19 type in handling disturbance analysis in graphical patterns is satisfactory.
Comparative Analysis of Breakdown Voltage, Temperature Rise, and Production Cost of Using Mineral Oil and Synthetic Ester in 33 MVA 132/33 kV Power Transformers Khusuma, AB Rendra; Indarto, Agus; Hudaya, Chairul; Setiabudy, Rudy; Husnayain, Faiz
International Journal of Electrical, Computer, and Biomedical Engineering Vol. 3 No. 2 (2025)
Publisher : Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62146/ijecbe.v3i2.139

Abstract

In support of achieving the net zero emission target in the power sector, the selection of environmentally friendly transformer insulating oil is very important. This study presents a comparative analysis of the dielectric and thermal performance between mineral oil and synthetic ester oil. The breakdown voltage (BDV) test was conducted with a variation of rest time of 1 minute and 10 minutes. In addition, temperature rise tests were conducted on a 33 MVA capacity power transformer with a voltage of 132/33 kV. Temperature rise testing is carried out on synthetic ester oil and mineral oil through thermal simulation with identical transformer specifications, the goal is that there are no distinguishing variables in the test. The test results show that at a rest time of 1 minute, synthetic ester oil produces fluctuating BDV values, with some data being below the minimum threshold of 60 kV according to IEC 61203 standards. In contrast, mineral oil (MO) showed stable and consistent dielectric performance. At a rest time of 10 minutes, both types of oil showed stable BDV values with low standard deviations. In terms of thermal performance, mineral oil produced a lower temperature rise than synthetic ester oil (SE), indicating better cooling efficiency. The study will also analyze the impact of transformer dimensions due to the different transformer oils used, which will result in the price of the transformer. The findings provide technical insights for manufacturers and users in selecting transformer oils that support environmental sustainability without compromising the reliability of power transformers.

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