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Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC)
Published by Institut Teknologi Dirgantara Adisutjipto
ISSN : 26852381     EISSN : 27152626     DOI : 10.28989/avitec
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering
This journal is the scientific publications journal published by Department of Electrical Engineering, Sekolah Tinggi Teknologi Adisutjipto. It aims to promote and disseminate the research finding in the development of management theories and practices. It will provide a platform for academicians, researchers, and practitioners to share their experience and solution to problems in different areas of journal scopes. Every submitted paper will be blind-reviewed by peer-reviewers. Reviewing process will consider novelty, objectivity, method, scientific impact, conclusion, and references.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 142 Documents
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Duval Triangle-Based Dissolved Gas Analysis using GNU Octave for Transformer Fault Detection Putri, Ervina Galuh Ika; Pravitasari, Deria; Yuliantari, Risky Via
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3156

Abstract

Transformers are vital components in electrical power systems. However, they are also susceptible to various types of failures, including thermal and electrical faults caused by the formation of electromotive force, which, if left unaddressed, may result in degradation of the oil insulation. One effective approach to mitigate such issues is to conduct feasibility testing and oil analysis, commonly known as Dissolved Gas Analysis (DGA), which examines the condition of the insulating fluid within the transformer. In this study, gas concentration levels were identified as follows: C₂H₄ = 9 ppm, CH₄ = 4 ppm, and C₂H₂ = 11 ppm. These values were visualized using the Duval Triangle Method, an established technique for analyzing gas content by measuring the concentration of three primary gases: Methane (CH₄), Ethylene (C₂H₄), and Acetylene (C₂H₂), all of which dissolve in the transformer oil. The advantage of this method lies in its ability to serve as an early fault detection tool for transformer oil. The analysis results indicated an electrical fault categorized as a High Energy Discharge in zone D2, identified by a single plotted point where the three gas lines intersect on the triangle diagram. This type of discharge is predominantly associated with Acetylene gas (C₂H₂) and is typically triggered by intense internal arcing within the transformer. The interpretation was further implemented using an automated data plotting system in GNU Octave, serving as a Transformer Fault Detection tool and computational software that utilizes the C++ programming language for data processing and visualization.
Design and Simulation of Optimized Load Frequency Control in Multi-Area Electrical Interconnection Systems Hasan, Ihsan Jabbar; Abed, Saif Ahmed; Salih, Nahla Abdul Jalil; Abdulkhaleq, Nadhir Ibrahim
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3082

Abstract

Maintaining frequency stability in modern interconnected power systems is critical for operational reliability, especially under varying load demands. Load Frequency Control (LFC) plays a pivotal role in balancing power exchanges and preserving nominal frequency across multi-area grids. This paper presents the design, modeling, and optimization of a two-area Load Frequency Control (LFC) system in interconnected power networks using MATLAB/Simulink. Each area comprises a governor, turbine, generator-load system, and a PID controller to regulate frequency deviations and maintain system stability following load disturbances. The study investigates the effects of key system parameters—including governor and turbine time constants, generator inertia, and tie-line coupling—on dynamic performance. To address mismatched responses between areas, Particle Swarm Optimization (PSO) is employed to tune system parameters and improve coordination. The optimization aims to minimize frequency deviations and tie-line power fluctuations while enhancing system response. Simulation results show that the proposed optimization approach significantly improves dynamic performance. Specifically, frequency deviations in both areas are reduced by over 55%, tie-line power fluctuation is minimized by 62.5%, and settling times for frequency responses are shortened by over 44%. These improvements demonstrate the effectiveness of the optimization strategy in enhancing inter-area coordination and system resilience. The framework also serves as a practical simulation-based educational tool for power engineering students and researchers to exploreLFC design and control strategies in multi-area systems.
Modeling and Optimization of 4G Pathloss using Swarm Intelligence Algorithm: Case Study and Python-Based Implementation Noviyansyah, Tri; Tahcfulloh, Syahfrizal
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3245

Abstract

Accurate pathloss (PL) modeling is critical for 4G-LTE network planning in complex urban environments like Central Tarakan, Indonesia. This study presents a Python-based, open-source implementation of Particle Swarm Optimization (PSO) to calibrate three conventional PL models, Okumura-Hata, SUI, and Ericsson 9999, using real drive-test data. Initial RMSE values exceeded 50 dB, revealing severe inaccuracies under heterogeneous terrain. PSO optimization dramatically improved accuracy: RMSE reduced to 5.98 dB (Okumura-Hata, 89.44% improvement), 9.83 dB (SUI, 84.03%), and 6.44 dB (Ericsson 9999, 91.32%). The optimized Okumura-Hata model achieved the highest reliability, with 88.89% of measurement points meeting the <8 dB threshold and the lowest standard deviation (1.71 dB). Ericsson 9999 attained the lowest minimum RMSE (0.06 dB), showcasing exceptional potential under favorable conditions. PSO converged rapidly within 50 iterations, and sensitivity analysis confirmed that standard parameters (ω = 0.5–0.7, c₁ = c₂ = 1.8–2.2) suffice for robust calibration, eliminating need for fine-tuning. Results demonstrate that real-world propagation deviates significantly from classical logarithmic assumptions, validating the necessity of data-driven, site-specific optimization. The fully open-source framework—built with NumPy, Pandas, and Matplotlib—offers a practical, scalable solution for intelligent radio planning in dynamic urban landscapes.
Optimization of BLDC Motor Geometry using Particle Swarm Optimization Algorithm to Achieve Efficiency Balance Across Various Electric Vehicle Traction Requirements Kurniawan, Kurniawan; Hasanudin, Hasanudin; Dwiyanto, Agus; Putra, Rivanda Tyaksa
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3119

Abstract

Gasoline vehicles (GVs) contribute significantly to global energy crises and environmental pollution, while electric vehicles (EVs) offer a more sustainable alternative. However, the current development and deployment of EVs are largely limited to ideal operating conditions, such as urban roads. To compete effectively with GVs, EVs must have drivetrain systems that maintain high efficiency even in non-ideal environments, including rural areas and rough terrains. This study proposes a geometry optimization method for a 1 kW Brushless DC (BLDC) motor to improve energy efficiency under three primary EV traction scenarios: climbing, acceleration, and cruising. The optimization targets nine geometric parameters—outer and inner stator radius, magnet thickness, rotor yoke thickness, shoe stator thickness, magnet width, shoe stator width, stator pole width, and back-iron thickness. The optimization is performed using a Particle Swarm Optimization (PSO) algorithm integrated with Finite Element Method Magnetics (FEMM) and analytical performance evaluation. The optimization constraints are derived from traction dynamics, weight, and volume limitations based on the regulations of the Indonesian Electric Vehicle Competition (Kompetisi Mobil Listrik Indonesia, KMLI). The results show that the optimized BLDC motor geometry can increase efficiency by up to 24.3% and torque by 11.3% compared to the baseline design. This research contributes a high-efficiency BLDC motor design tailored for dynamic EV traction demands under regulatory and extreme operational constraints, making it highly suitable for further development, including additional performance scenarios such as deceleration and cornering.
Classification Based on Artificial Neural Network for Regency Road Maintenance Priority Pratama, Bagus Gilang; Sari, Sely Novita; Yuliani, Oni
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3056

Abstract

The priority classification of road maintenance is an important issue in regional infrastructure management. This study developed a classification model based on Artificial Neural Network (ANN) to determine the priority of district road maintenance automatically based on actual condition data. The data covered 141 road sections, reduced from 15 to 9 main variables using Principal Component Analysis (PCA), and normalized with the Min-Max Scaler. The ANN model consists of 10 input neurons, 30 hidden neurons, and 5 priority class outputs. The data is divided in a 55-15-35 ratio for training, validation, and testing. The model produces 92% accuracy, 91.7% accuracy, 90.4% recall, and 90.9% F1-score. These findings demonstrate the reliability of ANN in multi-class classifications to support more efficient road maintenance decision-making. The novelty lies in the integration of actual field data, multi-criteria classification, and the application of ANN in the context of complex and underexplored district roads in the literature.
Machine Learning-based Chatbot Model for Healthcare Service: A Bibliometric Analysis Ekawati, Nia; Riadi, Imam; Yuliansyah, Herman
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3050

Abstract

While machine learning-based chatbots hold significant potential in healthcare services, a comprehensive synthesis regarding their roles, user demographics, benefits, and limitations remains unavailable, hindering in-depth understanding and future development. This study aims to conduct a bibliometric analysis to identify implementation trends and the research landscape of ML-based chatbot models in healthcare, simultaneously highlighting relevant existing gaps. Analysis of Scopus data using VOSviewer and “Publish or Perish” reveals “machine learning”, “chatbot” and “healthcare” as dominant keywords, indicating intensive research focus areas with stable publication growth. The United States emerges as a central hub for international research collaboration, particularly in AI for malnutrition; however, several outlier countries require further integration. Deep learning algorithms are identified as a crucial methodological trend for future directions. Chatbots possess the potential to revolutionize healthcare by enhancing accessibility and efficiency. Nevertheless, effective implementation necessitates careful consideration of ethical aspects, privacy, and data quality. The identified research gaps underscore the urgency for a holistic synthesis to guide responsible and effective chatbot innovation.
THD Minimization in Seven-Level Packed U-Cell (PUC) Inverter using Particle Swarm Optimization Amran, Osamah Abdullah Yahya; Windarko, Novie Ayub; Syarif, Iwan
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3352

Abstract

This study presents the modeling and simulation of an asymmetric seven-level Packed U-Cell (PUC) multilevel inverter employing a reduced number of power switches. A Modified Pulse Width Modulation (MPWM) scheme, optimized through the Particle Swarm Optimization (PSO) algorithm, is implemented to determine the optimal switching angles for enhanced harmonic elimination. The primary objective is to improve the output voltage waveform quality while reducing Total Harmonic Distortion (THD) and enhancing switching efficiency. The novelty of this work lies in integrating PSO with MPWM control in an asymmetric seven-level PUC inverter configuration with fewer switches, a combination that has not been previously addressed. Simulation results in Simulink demonstrate that the proposed PSO-optimized MPWM strategy achieves a THD of 17.72%, outperforming conventional modulation techniques. These findings highlight the effectiveness of intelligent optimization methods for multilevel inverter control and their potential contribution to improving power quality in renewable energy applications.
Smart Airport Radar: Multimodal AI Classification of Aerial Threats with Communication Link Performance Evaluation Abdulkhaleq, Nadhir Ibrahim; Hussein, Ahmed Saad
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3295

Abstract

The proliferation of small unmanned aerial vehicles (UAVs) near airports poses increasing risks to airspace safety and infrastructure security. This paper presents Smart Airport Radar, a simulation-based framework for classifying aerial threats — including drones, decoys, and birds — using multimodal AI features. The system emulates dynamic swarming behaviors and extracts five key descriptors — mean speed, heading variability, jerk, thermal signature, and radar cross-section (RCS) — to train a multiclass Support Vector Machine (SVM) classifier. Comparative analysis with a traditional RCS-based rule method shows the SVM achieving a classification accuracy of 93.33%, far outperforming the baseline at 20.00%. Radar-style trajectory visualizations and class-specific precision, recall, and F1-scores confirm the model’s robustness and interpretability. Beyond sensing and classification, the framework incorporates a communication link performance evaluation, analyzing classification accuracy under varying Signal-to-Noise Ratio (SNR) levels. Results reveal that maintaining link quality above 15 dB SNR preserves near-optimal detection performance, bridging radar sensing with wireless communication reliability. With minimal computational overhead, high adaptability, and strong cross-domain relevance, the proposed system offers a robust, explainable, and deployable solution for real-time perimeter defense in modern airport security infrastructures.
AI-Powered Mobile Proctoring Frameworks using Machine Learning Algorithms in Higher Education: Post-Covid Trends, Challenges, and Ethical Implications Mogoi, Bartholomew Oganda; Kamau, John; Ongus, Raymond
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3600

Abstract

The rapid transition to online learning during and after the COVID-19 (Corona Virus Disease) pandemic has heightened the need for secure, scalable, and ethical online exam systems. AI-powered mobile proctoring frameworks have emerged as viable alternatives to traditional invigilation methods, enabling automated anomaly detection and behavior analysis through machine learning algorithms. This systematic review examines post-COVID trends, technological developments, challenges, and ethical implications of mobile AI proctoring in higher education. Following PRISMA 2020 guidelines, 180 studies were retrieved and screened, with 20 peer-reviewed articles meeting the inclusion criteria. Findings reveal that while AI-powered proctoring enhances scalability, integrity, and real-time monitoring, it raises significant concerns about privacy, algorithmic bias, accessibility, and technical reliability. The review identifies gaps in relation to technical and methodological issues, ethical and social concerns, and institutional and infrastructural readiness. This review illustrates a lapse in the existing literature, which focus on resource intensive proctoring frameworks without considering mobile compatibility and light-weight frameworks, discusses technical challenges, and recommends future research directions to balance technological effectiveness with ethical standards.
Ground Movement Tracking System for Airside Operations using Global Navigation Satellite System (GNSS) and Long Range (LoRa) Communication Wisnuardana, Cokorda Dwija; Wahyudi, Johan; Sulaiman, Muhammad Arif
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3544

Abstract

Accurate monitoring of ground vehicle and aircraft movement in the airport airside is essential to reduce the risk of incidents caused by limited coordination between controllers, pilots, and ground operators. This study proposes the design of a ground movement tracking system for airside operations, utilizing a Global Navigation Satellite System (GNSS) module (Matek M10Q-5883) and a Long Range (LoRa) communication module (LILYGO ESP32), integrated with a web dashboard for real-time visualization. The system was developed using the Waterfall methodology, covering requirement analysis, system design, hardware and software implementation, and performance testing in simulated airside conditions. Experimental results show that the system achieved an average coordinate deviation of only 0.000017º equivalent to about 1.7 meters  compared to a reference device, and maintained reliable data transmission up to 1.4 km under line-of-sight conditions. These findings demonstrate that the proposed system provides accurate and stable monitoring of ground movements, offering a cost-efficient and weather-independent alternative to GSM-based solutions. In addition, by optimizing LoRa parameters, the system successfully extended its communication range beyond the ~1 km typically reported in related studies, highlighting its novelty and contribution to enhanced safety in airport operations.

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