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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 10 Documents
 1 
Search results for , issue "Vol 7, No 3 (2025): November (Special Issue)" : 10 Documents clear
A Systematic Review of Surgical Robots and Controls for Teleoperations Assan, Kojo Nyamekye; Wayoe, Phyllis Teteki; Ayiku, Christabel Naadu; Aku, Agbemor Dzifa; Quagraine, Herbert
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.v7i3.2941

Abstract

The advancements in healthcare technology have transformed the landscape of medical practice, with robotics emerging as a prominent feature. The use of teleoperation, particularly in remote robot-assisted surgeries, has obtained significant attention and acclaim for its potential to enhance surgical outcomes and expand access to specialized care. Despite the increase in research in this domain, there remains a need for a thorough and systematic review to consolidate the diverse findings and conclusions. This work presents an organized synthesis of existing related works using Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), facilitating easier access to vital information for researchers, practitioners, and innovators. To achieve this, the historical development of surgical robots were explored. Furthermore, different types of surgical robots with various architecture were extensively examined to highlight the relevance of their application. Imperatively, advancements in teleoperability and control systems of surgical robots were comprehensively discussed underscoring its growing influence in current healthcare delivery. Moreover, practical challenges faced by teleoperated surgical robots were highlighted and elaborated to point out their limitations. Additionally, future directions aimed to tackle the identified shortfalls in robotic surgery and teleoperation were considered. In this regard, this work provided an impactful contribution that positively influences growth in the area of robotic surgery and teleoperation by consolidating several insights into a cohesive framework which ultimately seek to improve patient care.
Speed Control Analysis of Frequency Changes in Three Phase Synchronous Motor with Variable Speed Drive (VSD) Lasmana, Lasmana; Musyaffa, Muhammad Aziz; Taryo, Taryo
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.v7i3.3153

Abstract

This study investigates the impact of frequency adjustment using a Variable Speed Drive (VSD) on the performance of a three-phase AC synchronous motor under both no-load and load conditions. Energy inefficiency in industrial systems often results from mismatches between motor speed and load demands. The motor was tested at frequencies ranging from 20 Hz to 50 Hz to evaluate changes in speed, input power, torque, and efficiency. Unlike previous studies that focused solely on motor speed, this research provides a more comprehensive performance analysis. The results show that increasing frequency leads to higher motor speed and power consumption, but a decrease in torque. Under no-load conditions, speed increased from 607 RPM at 20 Hz to 1506 RPM at 50 Hz, while torque dropped from 1.57 Nm to 0.63 Nm. Under load, speed increased from 88 RPM to 683 RPM, and torque declined from 10.9 Nm to 1.39 Nm. Although motor efficiency decreases at higher frequencies due to increased magnetizing current caused by the constant V/f ratio, it must be emphasized that VSDs can significantly enhance energy efficiency by allowing the motor to operate at an optimal speed according to the load, instead of continuously running at full speed. Therefore, dynamic frequency control based on load variation is essential to optimize motor performance. VSDs thus play a vital role in intelligent control strategies aimed at improving energy efficiency in industrial applications.
Portable ECG Prototype based on Arduino and Random Forest Classification for Home Heart-Rate Monitoring Nugraha, R. Ferdy Akbar; Tindaon, Novendy Alberto Will; Susena, Arya; Duandes, Alfonso; Ridwan, Achmad
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.v7i3.3144

Abstract

Electrocardiogram (ECG) examination is essential for detecting heart rhythm disorders, yet limited access and high costs often prevent routine medical check-ups for many people. This study addresses these obstacles by designing and developing a portable ECG prototype capable of independent home-based heart monitoring. The system integrates an AD8232 sensor for signal acquisition, an Arduino Uno microcontroller as the main processor, and a simplified Random Forest classification algorithm to distinguish between normal, bradycardia, and tachycardia conditions. Measurement results are saved in CSV format on an SD card, then visualized and analyzed using Jupyter Notebook. The prototype was tested on 100 samples in a static and relaxed state to ensure signal stability. Its heartbeat classification achieved an accuracy of 99.0%, slightly higher than the PTB-XL reference dataset’s 98.0%, and consistent with results reported by recent TinyML- and Random Forest-based ECG studies. Unlike prior IoT-based frameworks, this work combines cost-effective microcontroller hardware with simplified offline on-device classification for practical daily monitoring without continuous cloud access. These findings confirm that the proposed system can produce reliable readings approaching clinical standards while remaining simple, affordable with a component cost under USD 31, and accessible for routine public heart health screening.
Multi-Class Facial Acne Classification using the EfficientNetV2-S Deep Learning Model Pramono, Aldi Yogie; Kusnawi, Kusnawi
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.v7i3.3157

Abstract

Acne vulgaris is a common dermatological condition that significantly impacts psychosocial well-being, particularly among adolescents and young adults. Accurate identification of acne lesion types is crucial for effective treatment planning, yet manual assessment by dermatologists is subjective and resource-intensive. This study proposes a Convolutional Neural Network (CNN)-based approach using EfficientNetV2-S with transfer learning and data augmentation to perform multi-class classification of five acne lesion types: blackheads, whiteheads, papules, pustules, and cysts. The model was trained and evaluated on 4,673 annotated facial images, achieving an accuracy of 96.66%, outperforming conventional lightweight CNNs and achieving comparable results to heavier ensemble architectures. Statistical validation using p-values and effect sizes confirms the model’s robustness. The scientific contribution of this research lies in the integration of EfficientNetV2-S with a customized classification head optimized for multi-class acne recognition—an area underexplored in dermatological AI research. Unlike previous works focusing on binary classification or ensemble models, our approach offers a lightweight, accurate, and scalable solution for real-world teledermatology, thus establishing a novel benchmark in multi-class acne classification.
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.

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