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All Journal Jurnal Buana Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika U Karst JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Informatika Universitas Pamulang Jiko (Jurnal Informatika dan komputer) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science Buletin Ilmiah Sarjana Teknik Elektro Jurnal Informatika Ekonomi Bisnis Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Yumary: Jurnal Pengabdian kepada Masyarakat International Journal of Robotics and Control Systems KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Brilliance: Research of Artificial Intelligence Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika Ekonomi Bisnis Jurnal Pengabdian Masyarakat Charitas Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Airlangga, Gregorius
Atma Jaya Catholic University Of Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Advancing UAV Path Planning System: A Software Pattern Language for Dynamic Environments Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9407

Abstract

In the rapidly advancing domain of Unmanned Aerial Vehicle (UAV) technologies, the capability to navigate dynamic and unpredictable environments is paramount. To this end, we present a novel design pattern framework for real-time UAV path planning, derived from the established Pattern Language of Program Community (PLOP). This framework integrates a suite of software patterns, each selected for its role in enhancing UAV operational adaptability, environmental awareness, and resource management. Our proposed framework capitalizes on a blend of behavioral, structural, and creational patterns, which work in concert to refine the UAV's decision-making processes in response to changing environmental conditions. For instance, the Observer pattern is employed to maintain real-time environmental awareness, while the Strategy pattern allows for dynamic adaptability in the UAV's path planning algorithm. Theoretical analysis and conceptual evaluations form the backbone of this research, eschewing empirical experiments for a detailed exploration of the design's potential. By offering a systematic and standardized approach, this research contributes to the UAV field by providing a robust theoretical foundation for future empirical studies and practical implementations, aiming to elevate the efficiency and safety of UAV operations in dynamic environments.
Adaptive Cyber-Defense for Unmanned Aerial Vehicles: A Modular Simulation Model with Dynamic Performance Management Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9415

Abstract

In light of escalating cyber threats, this study tackles the cybersecurity challenges in UAV systems, underscoring the limitations of static defense mechanisms. Traditional security approaches fall short against the sophisticated and evolving nature of cyber-attacks, particularly for UAVs that depend on real-time autonomy. Addressing this deficiency, we introduce an adaptive modular security system tailored for UAVs, enhancing resilience through real-time defensive adaptability. This system integrates scalable, modular components and employs machine learning techniques—specifically, neural networks and anomaly detection algorithm to improve threat prediction and response. Our approach marks a significant leap in UAV cybersecurity, departing from static defenses to a dynamic, context-aware strategy. By employing this system, UAV stakeholders gain the flexibility needed to counteract multifaceted cyber risks in diverse operational scenarios. The paper delves into the system's design and operational efficacy, juxtaposing it with conventional strategies. Experimental evaluations, using varied UAV scenarios, measure defense success rates, computational efficiency, and resource utilization. Findings reveal that our system surpasses traditional models in defense success and computational speed, albeit with a slight increase in resource usage a consideration for deployment in resource-constrained contexts. In closing, this research underscores the imperative for dynamic, adaptable cybersecurity solutions in UAV operations, presenting an innovative and proactive defense framework. It not only illustrates the immediate benefits of such adaptive systems but also paves the way for ongoing enhancements in UAV cyber defense mechanisms.
Comparative Analysis of MLP, CNN, and RNN Models in Automatic Speech Recognition: Dissecting Performance Metric Lenson, Abraham K. S.; Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9668

Abstract

This study conducts a comparative analysis of three prominent machine learning models: Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) in the field of automatic speech recognition (ASR). This research is distinct in its use of the LibriSpeech 'test-clean' dataset, selected for its diversity in speaker accents and varied recording conditions, establishing it as a robust benchmark for ASR performance evaluation. Our approach involved preprocessing the audio data to ensure consistency and extracting Mel-Frequency Cepstral Coefficients (MFCCs) as the primary features, crucial for capturing the nuances of human speech. The models were meticulously configured with specific architectural details and hyperparameters. The MLP and CNN models were designed to maximize their pattern recognition capabilities, while the RNN (LSTM) was optimized for processing temporal data. To assess their performance, we employed metrics such as precision, recall, and F1-score. The MLP and CNN models demonstrated exceptional accuracy, with scores of 0.98 across these metrics, indicating their effectiveness in feature extraction and pattern recognition. In contrast, the LSTM variant of RNN showed lower efficacy, with scores below 0.60, highlighting the challenges in handling sequential speech data. The results of this study shed light on the differing capabilities of these models in ASR. While the high accuracy of MLP and CNN suggests potential overfitting, the underperformance of LSTM underscores the necessity for further refinement in sequential data processing. This research contributes to the understanding of various machine learning approaches in ASR and paves the way for future investigations. We propose exploring hybrid model architectures and enhancing feature extraction methods to develop more sophisticated, real-world ASR systems. Additionally, our findings underscore the importance of considering model-specific strengths and limitations in ASR applications, guiding the direction of future research in this rapidly evolving field.
Optimizing UAV Navigation: A Particle Swarm Optimization Approach for Path Planning in 3D Environments Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9696

Abstract

This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges.
Enhancing UAV Navigation in Dynamic Environments: A Detailed Integration of Fick's Law Algorithm for Optimal Pathfinding in Complex Terrains Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9697

Abstract

In the realm of Unmanned Aerial Vehicles (UAVs), efficient navigation in complex environments is crucial, necessitating advanced pathfinding algorithms. This study introduces the Fick's Law Algorithm (FLA) for UAV path optimization, drawing inspiration from the principles of molecular diffusion, and positions it in the context of existing algorithms such as A* and Dijkstra's. Through a comparative analysis, we highlight FLA's unique approach and advantages in terms of computational efficiency and adaptability to dynamic obstacles. Our experiment, conducted in a simulated three-dimensional space with static and dynamic obstacles, involves an extensive quantitative analysis. FLA's performance is quantified through metrics like path length reduction, computation time, and obstacle avoidance efficacy, demonstrating a marked improvement over traditional methods. The technical foundation of FLA is detailed, emphasizing its iterative adaptation based on a cost function that accounts for path length and obstacle avoidance. The algorithm's rapid convergence towards an optimal solution is evidenced by a significant decrease in the cost function, supported by data from our convergence graph. Visualizations in both 2D and 3D effectively illustrate the UAV’s trajectory, highlighting FLA's efficiency in real-time path correction and obstacle negotiation. Furthermore, we discuss FLA's practical implications, outlining its adaptability in various real-world UAV applications, while also acknowledging its limitations and potential challenges. This exploration extends FLA's relevance beyond theoretical contexts, suggesting its efficacy in real-world scenarios. Looking ahead, future work will not only focus on enhancing FLA's computational efficiency but also on developing specific methodologies for real-world testing. These include adaptive scaling for different UAV models and environments, as well as integration with UAV hardware systems. Our study establishes FLA as a potent tool for autonomous UAV navigation, offering significant contributions to the field of dynamic path optimization.
Comparative Analysis of Machine Learning Models for Tree Species Classification from UAV LiDAR Data Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i1.10059

Abstract

Forest ecosystems play a pivotal role in maintaining global biodiversity and climate balance. The precise identification of tree species via remote sensing technologies is vital for effective ecological surveillance and forest stewardship. This research conducts a comparative analysis of various machine learning algorithms for the binary classification of tree species utilizing LiDAR data captured by Unmanned Aerial Vehicles (UAVs). We analyzed a dataset featuring 192 trees from a diverse forest, employing models such as Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Gradient Boosting, and Decision Trees. These models were assessed on their accuracy, precision, recall, and F1-scores to ascertain their efficacy. Our findings reveal that Logistic Regression and SVM were superior, achieving precision and recall scores up to 0.96, indicating their robust predictive capability. In contrast, KNN underperformed, suggesting the need for parameter refinement. Although ensemble methods demonstrated resilience, they were more prone to overfitting in comparison to the more straightforward Logistic Regression and SVM models. Preliminary data preprocessing and feature engineering techniques are discussed, enhancing the models' performance. This work enriches the domain of remote sensing and ecological monitoring by offering an in-depth evaluation of machine learning models for tree species classification, underscoring their advantages and constraints. It underscores the transformative potential of machine learning in refining ecological analysis precision, thereby aiding in the pursuit of sustainable forest management. Future research directions could include model refinement through advanced feature selection or the exploration of novel machine learning algorithms for improved classification accuracy.
Performance Evaluation of Deep Learning Techniques in Gesture Recognition Systems Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i1.10120

Abstract

As human-computer interaction becomes increasingly sophisticated, the significance of gesture recognition systems has expanded, impacting diverse sectors such as healthcare, smart device interfacing, and immersive gaming. This study conducts an in-depth comparison of seven cutting-edge deep learning models to assess their capabilities in accurately recognizing gestures. The analysis encompasses Long Short-Term Memory Networks (LSTMs), Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs), Simple Recurrent Neural Networks (RNNs), Multi-Layer Perceptrons (MLPs), Bidirectional LSTMs (BiLSTMs), and Temporal Convolutional Networks (TCNs). Evaluated on a dataset representative of varied human gestures, the models were rigorously scored based on accuracy, precision, recall, and F1 metrics, with LSTMs, GRUs, BiLSTMs, and TCNs outperforming others, achieving an impressive score bracket of 0.93 to 0.95. Conversely, MLPs trailed with scores around 0.59 to 0.60, underscoring the challenges of non-temporal models in processing sequential data. This study pinpoints model selection as pivotal for optimal system performance and suggests that recognizing the temporal patterns in gesture sequences is crucial. Limitations such as dataset diversity and computational demands were noted, emphasizing the need for further research into models' operational efficiencies. Future studies are poised to explore hybrid models and real-time processing, with the prospect of enhancing gesture recognition systems' interactivity and accessibility. This research thus provides a foundational benchmark for selecting and implementing the most suitable computational methods for advancing gesture recognition technologies.
Predicting Urban Happiness: A Comparative Analysis of Deep Learning Models Airlangga, Gregorius
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28801

Abstract

This study explores the efficacy of various deep learning models in predicting urban happiness scores, a critical indicator of the quality of life in urban environments. Recognizing the complex interplay of factors contributing to urban happiness, we employed a suite of models, including Dense Neural Networks (DNN), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), Autoencoders, Multi-Layer Perceptron with Dropout (MLP Dropout), and Simple Recurrent Neural Networks (RNN), to analyze a comprehensive dataset encompassing environmental quality, socio-economic factors, and urban infrastructure. Our methodology centered on rigorous data preprocessing to ensure integrity and usability, followed by a detailed comparative analysis of model performances based on Root Mean Squared Error (RMSE) metrics. The results revealed that the CNN model outperformed others in identifying spatial patterns crucial for urban happiness, indicating its superior capability in processing complex urban data. In contrast, the LSTM model showed less accuracy, suggesting a nuanced understanding of temporal data's role in predicting urban happiness. This research not only sheds light on the potential of deep learning in urban studies but also offers valuable insights for urban planners and policymakers aiming to enhance urban living conditions. Through this comparative analysis, our study contributes to the growing discourse on leveraging advanced data analytics for urban planning and opens avenues for future research into the integration of diverse data sources and model hybridization to refine urban happiness predictions.
Enhancing Electric Vehicle Range Prediction through Deep Learning: An Autoencoder and Neural Network Approach Airlangga, Gregorius
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28803

Abstract

The burgeoning adoption of electric vehicles (EVs) signifies a pivotal shift towards sustainable transportation, necessitated by the global imperative to mitigate climate change impacts. Central to this transition is the resolution of range anxiety, a significant barrier impeding wider EV acceptance. This research introduces a novel deep learning framework combining autoencoders and deep neural networks (DNNs) to predict EV range more accurately and reliably. Leveraging a comprehensive dataset from the "Electric Vehicle Population Data," we embarked on a meticulous process of data cleaning, feature engineering, and preprocessing to prepare the dataset for analysis. The study innovatively applies an autoencoder for unsupervised feature learning, effectively reducing dimensionality and extracting salient features from high-dimensional EV data. Subsequently, a DNN model utilizes these features to predict the EV range, offering insights into the vehicle's performance across various conditions. Employing a 10-fold cross-validation approach, the model's efficacy is rigorously evaluated, ensuring robustness and generalizability of the predictions. Our methodology demonstrates a significant enhancement in prediction accuracy compared to conventional machine learning models, as evidenced by the Mean Squared Error (MSE) metric. This research not only contributes to the academic discourse on sustainable transportation and deep learning applications but also provides practical insights for manufacturers, policymakers, and consumers aiming to navigate the complexities of EV adoption and infrastructure development. By addressing the critical challenge of range prediction, this study paves the way for advancing EV analytics, ultimately supporting the transition to a more sustainable and efficient transportation ecosystem.
COMPARISON OF DEEP LEARNING ARCHITECTURES FOR ANEMIA CLASSIFICATION USING COMPLETE BLOOD COUNT DATA Airlangga, Gregorius
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2109

Abstract

Anemia is a common condition marked by a deficiency in red blood cells or hemoglobin, affecting the body's ability to deliver oxygen to tissues. Accurate and timely diagnosis is essential for effective treatment. This study aims to classify different types of anemia using complete blood count (CBC) data through the application of deep learning models. We evaluated the performance of four deep learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Fully Connected Network (FCN). The dataset included CBC parameters such as hemoglobin, platelet count, and white blood cell count, labeled with anemia types. Our results indicate that CNN and FCN models achieved the highest test accuracies of 0.85, outperforming MLP and RNN models. This superior performance is due to the ability of CNN and FCN to capture complex patterns and spatial relationships within CBC data. Techniques like data augmentation and weighted loss functions were employed to address class imbalance. These findings demonstrate the potential of deep learning models to automate anemia diagnosis, thereby enhancing clinical decision-making and patient outcomes.
Co-Authors Afandi Nur Aziz Thohari, Afandi Nur Aziz Alamsyah - Amalia, Andika Elok Basuki, Widodo Widjaja Bata, Julius Victor Manuel Christanto, Henoch Juli Christiany Suwartono Denny Jean Cross Sihombing Denny Jean Cross Sihombing Eugenius Kau Suni Henoch Juli Christanto Julius Bata Julius Victor Manuel Bata Khairul Huda Kristian, Yoel Lenson, Abraham K. S. Lim, Bobi Hartanto Pramudita Makin, See Jong Nugroho, Oskar Ika Adi Pedro Manuel Lamberto Buu Sada Philia, Gregoria Piolo, Samuel Pujo Hari Saputro Rahmananta, Radyan Ronald Sukwadi Setiawan, Yohanes Evan Sidhunata, Billy Macarius Stephen Aprius Sutresno Sugianto, Lai Ferry Suni, Eugenius Kau Wicaksana, Hilman Singgih