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All Journal Buletin Ilmiah Sarjana Teknik Elektro Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) International Journal of Robotics and Control Systems Control Systems and Optimization Letters
Nugroho, Oskar Ika Adi
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Comparative Analysis of Path Planning Algorithms for Multi-UAV Systems in Dynamic and Cluttered Environments: A Focus on Efficiency, Smoothness, and Collision Avoidance Sukwadi, Ronald; Airlangga, Gregorius; Basuki, Widodo Widjaja; Kristian, Yoel; Rahmananta, Radyan; Sugianto, Lai Ferry; Nugroho, Oskar Ika Adi
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1555

Abstract

This study evaluates the performance of various path planning algorithms for multi-UAV systems in dynamic and cluttered environments, focusing on critical metrics such as path length, path smoothness, collision avoidance, and computational efficiency. We examined several algorithms, including A*, Genetic Algorithm, Modified A*, and Particle Swarm Optimization (PSO), using comprehensive simulations that reflect realistic operational conditions. Key evaluation metrics were quantified using standardized methods, ensuring the reproducibility and clarity of the findings. The A* Path Planner demonstrated efficiency by producing the shortest and smoothest paths, albeit with minor collision avoidance limitations. The Genetic Algorithm emerged as the most robust, balancing path length, smoothness, and collision avoidance, with zero violations and high feasibility. Modified A* also performed well but exhibited slightly less smooth paths. In contrast, algorithms like Cuckoo Search and Artificial Immune System faced significant performance challenges, especially in adapting to dynamic environments. Our findings highlight the superior performance of the Genetic Algorithm and Modified A* under these specific conditions. We also discuss the potential for hybrid approaches that combine the strengths of these algorithms for even better performance. This study's insights are critical for practitioners looking to optimize multi-UAV systems in challenging scenarios.
UAV Logistics Pattern Language for Rural Areas Rahmananta, Radyan; Airlangga, Gregorius; Sukwadi, Ronald; Basuki, Widodo Widjaja; Sugianto, Lai Ferry; Nugroho, Oskar Ika Adi; Kristian, Yoel
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1554

Abstract

The logistical challenges in rural areas, which often face limited infrastructure, varied terrains, and dispersed populations, often lead to inefficient and costly delivery systems. Recent developments in Unmanned Aerial Vehicle (UAV) technology offer a theoretical framework for overcoming these challenges. This research proposes a comprehensive pattern language specifically designed for multi-UAV logistics operations in rural settings. The proposed system integrates critical components such as LiDAR-based map generation, altitude information storage, partial goal estimation, and collision avoidance into a unified framework. Unlike existing research that typically focuses on isolated aspects like route optimization or payload management, this system features an advanced path planning algorithm capable of real-time environmental assessment and direction-aware navigation. Focus group discussions with logistics experts from Talaud Island, North Sulawesi, Indonesia informed the design and refinement of the proposed patterns, ensuring that they address the practical needs of rural logistics. Our analysis suggests that this system offers a theoretical foundation for significantly improving the efficiency, reliability, and sustainability of delivering essential goods and services to rural areas, thereby supporting equitable development and improving the quality of life in these communities. While no empirical data is presented, the framework serves as a scalable foundation for future implementations of UAV-based rural logistics systems.
Control of Water Flow Rate in a Tank Using the Integral State Feedback Based on Arduino Uno Hendriyanto, Raeyvaldo Dwi; Puriyanto, Riky Dwi; Ma'arif, Alfian; Vera, Marco Antonio Márquez; Nugroho, Oskar Ika Adi; Chivon, Choeung
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.162

Abstract

In the industrial world, many tools have been made to facilitate human work in carrying out control and measurement that is made automatically in a production process. Because in some parts of a production process in the industry that is done manually is no longer effective so that accurate and precise automatic control is needed. The control that will be used in this study is the Integral State Feedback (ISF) control with Arduino Uno as a microcontroller to design and run the system. The actuator used is a 12V water pump with the sensor used is the YF-S401. The system will run the ISF control as long as the data is less than 300 and if it reaches 300 data, the system will stop processing the ISF control and turn off the 12V water pump. The sensor reading error obtained is 27%. Parameters Ki = 0.3, K1 = 6, and K2 = 2 obtained from MATLAB Simulink can be applied to the research tool but have a slow system response Delay Time and Rise Time, so the researcher made a modification parameter with a value of Ki = 1, K1 = 6, and K2 = 2 and obtained a faster system response Delay Time and Rise Time. So it can be concluded that the best parameters for this study use modified parameters.
Comparative Evaluation of Machine Learning Models for UAV Network Performance Identification in Dynamic Environments Airlangga, Gregorius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i4.12409

Abstract

The rapid integration of Unmanned Aerial Vehicles (UAVs) into critical applications such as disaster management, logistics, and communication networks has brought forth significant challenges in optimizing their performance under dynamic and unpredictable conditions. This study addresses these challenges by systematically evaluating the predictive capabilities of multiple machine learning models for UAV network performance identification. Models including RandomForest, GradientBoosting, Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), AdaBoost, ExtraTrees, LogisticRegression, and DecisionTree were analyzed using comprehensive metrics such as average accuracy, macro F1-score, macro precision, and macro recall. The results demonstrated the superiority of ensemble methods, with ExtraTrees achieving the highest performance across all metrics, including an accuracy of 0.9941. Other ensemble models, such as RandomForest and GradientBoosting, also showcased strong results, emphasizing their reliability in handling complex UAV datasets. In contrast, non-ensemble approaches such as LogisticRegression and MLP exhibited comparatively lower performance, suggesting their limitations in generalization under dynamic conditions. Preprocessing techniques, including SMOTE for addressing class imbalances, were applied to enhance model reliability. This research highlights the importance of ensemble learning techniques in achieving robust and balanced UAV performance predictions. The findings provide actionable insights into model selection and optimization strategies, bridging the gap between theoretical advancements and real-world UAV deployment. The proposed methodology and results have impact for advancing UAV technologies in critical, network performance-sensitive applications.
Deep Learning Approaches for Water Quality Prediction in Aquaponics Systems: A Comparative Study of Recurrent and Feedforward Architectures Airlangga, Gregorius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i1.12411

Abstract

Accurate prediction of water quality parameters is critical for the effective management and sustainability of aquaponics systems. This study evaluates the performance of four deep learning architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (SimpleRNN), and Dense Neural Network (DenseNN) for forecasting key water quality parameters, including temperature, turbidity, dissolved oxygen, pH, ammonia, and nitrate. A significant research gap is addressed by analyzing how these models perform on noisy and minimally preprocessed datasets, advancing prior studies that lack robust preprocessing techniques tailored for aquaponics systems. A ten-fold cross-validation framework was employed to rigorously assess the models, with Mean Squared Error (MSE) and Mean Absolute Error (MAE) as evaluation metrics. The results demonstrate that LSTM and GRU models outperform other architectures, achieving average validation losses of 0.0028 and 0.0028, respectively, and mean absolute errors of 0.0473 and 0.0478. These models effectively capture the temporal dependencies inherent in time-series data, making them highly suitable for the complex dynamics of aquaponics systems. Unlike previous studies, this research highlights the trade-offs between computational efficiency and predictive accuracy in these models. In contrast, the SimpleRNN model exhibited higher error rates due to its inability to model long-term dependencies, while the DenseNN model, lacking temporal processing mechanisms, showed the lowest performance with an average validation loss of 0.0075 and MAE of 0.0797. This study underscores the importance of selecting appropriate model architectures for time-series forecasting tasks and provides a foundation for deploying predictive systems to optimize aquaponics operations. Future work includes exploring hybrid models with attention mechanisms and real-time data integration for enhanced operational efficiency.
Neural Network Architectures for UAV Path Planning: A Comparative Study with A* Algorithm as Benchmark Airlangga, Gregorius; Bata, Julius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1757

Abstract

Autonomous path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in applications ranging from disaster response to urban logistics. Traditional algorithms, such as A*, are widely recognized for their reliability in generating collision-free and efficient trajectories but often struggle with scalability in complex and dynamic environments. This study evaluates the performance of several neural network architectures, including MLP-LSTM, CNN-GRU, CNN-LSTM, CNN BILSTM, and others, as potential alternatives to classical methods. A dataset of trajectories generated by the A* algorithm was used to train and benchmark the models, enabling direct performance comparison across key metrics such as path length, smoothness, clearance, collisions, and waypoint density. The results demonstrate that the MLP-LSTM model outperforms other neural architectures, producing paths that closely resemble A* trajectories with high smoothness and waypoint granularity. While some models, such as CNN-GRU and CNN-BILSTM, show promise in generating feasible paths, their performance is inconsistent across different UAV scenarios. Models like Residual CNN and Hybrid CNN-MHA failed to generate meaningful trajectories, highlighting the critical importance of architectural choices. This study underscores the potential of neural network models for UAV path planning.
Enhancing UAV Communication Security: Multi-Label Anomaly Detection Using Machine Learning in Imbalanced Data Environments Airlangga, Gregorius; Sihombing, Denny Jean Cross; Nugroho, Oskar Ika Adi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.883

Abstract

Unmanned Aerial Vehicle (UAV) communication networks are increasingly vulnerable to cyber threats, including spoofing, jamming, malware, and distributed denial-of-service (DDoS) attacks. Effective anomaly detection is crucial to maintaining network integrity and operational security. This study evaluates multiple machines learning models, including Support Vector Machines, Logistic Regression, XGBoost, Gradient Boosting, and Random Forest, to detect anomalies in UAV communication networks. A real-world dataset containing 44,016 instances of network telemetry and security indicators was utilized, with each instance labeled for multiple potential anomalies. Experimental results reveal a significant class imbalance, where models achieve high accuracy (92%) but fail to detect minority class anomalies, yielding near-zero recall scores for critical cyber threats. The study highlights the limitations of traditional classifiers in imbalanced multi-label classification tasks and emphasizes the need for advanced techniques such as Synthetic Minority Over-sampling (SMOTE), cost-sensitive learning, and deep learning-based anomaly detection. The findings suggest that conventional machine learning approaches alone are insufficient for reliable anomaly detection in UAV networks, necessitating hybrid solutions that integrate multiple detection paradigms. Future work should explore adaptive ensemble learning methods and deep anomaly detection frameworks to improve recall and precision for rare cybersecurity threats.
Enhanced Advanced Multi-Objective Path Planning (EAMOPP) for UAV Navigation in Complex Dynamic 3D Environments Airlangga, Gregorius; Bata, Julius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry; Saputro, Pujo Hari; Makin, See Jong; Alamsyah, Alamsyah
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1759

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as vital tools in diverse applications, including disaster response, surveillance, and logistics. However, navigating complex, obstacle-rich 3D environments with dynamic elements remains a significant challenge. This study presents an Enhanced Advanced Multi-Objective Path Planning (EAMOPP) model designed to address these challenges by improving feasibility, collision avoidance, and path smoothness while maintaining computational efficiency. The proposed enhancement introduces a hybrid sampling strategy that combines random sampling with gradient-based adjustments and a refined cost function that prioritizes obstacle avoidance and path smoothness while balancing path length and energy efficiency. The EAMOPP was evaluated in a series of experiments involving dynamic environments with high obstacle density and compared against baseline algorithms, including A*, RRT*, Artificial Potential Field (APF), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Results demonstrate that the EAMOPP achieves a feasibility score of 0.9800, eliminates collision violations, and generates highly smooth paths with an average smoothness score of 9.3456. These improvements come with an efficient average execution time of 6.6410 seconds, outperforming both traditional and heuristic-based methods. Visual analyses further illustrate the model's ability to navigate effectively through dynamic obstacle configurations, ensuring reliable UAV operation. Future research will explore optimizations to further enhance the model's applicability in real-world UAV missions.
Co-Authors Airlangga, Gregorius Alamsyah - Alfian Ma’arif Basuki, Widodo Widjaja Chivon, Choeung Denny Jean Cross Sihombing Hendriyanto, Raeyvaldo Dwi Julius Bata Kristian, Yoel Makin, See Jong Pujo Hari Saputro Puriyanto, Riky Dwi Rahmananta, Radyan Ronald Sukwadi Sugianto, Lai Ferry Vera, Marco Antonio Marquez