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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-SAKTI (Jurnal Sains Komputer dan Informatika) 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) 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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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.
Comparative Analysis of Voting and Stacking Ensemble Learning for Heart Disease Prediction: A Machine Learning Approach Airlangga, Gregorius
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i1.2584

Abstract

Heart disease remains a leading cause of mortality worldwide, necessitating the development of accurate predictive models for early diagnosis and intervention. This study investigates the effectiveness of ensemble learning approaches, particularly Voting and Stacking classifiers, in comparison to traditional machine learning models and deep learning architectures. Using a dataset containing clinical and diagnostic attributes, preprocessing steps such as label encoding and standardization were applied to ensure compatibility with machine learning models. The ensemble classifiers were constructed using base learners, including Random Forest, Gradient Boosting, and XGBoost, with soft voting aggregation and logistic regression meta-learning for the Stacking approach. The models were evaluated using stratified ten-fold cross-validation based on precision, recall, F1-score, and ROC-AUC. The results indicate that the Voting classifier achieved the highest overall F1-score (0.8882) and ROC-AUC (0.8697), surpassing the Stacking classifier. Additionally, ensemble models demonstrated competitive performance compared to deep learning architectures, with Random Forest and Gradient Boosting achieving the highest ROC-AUC scores of 0.9313 and 0.9279, respectively. The findings suggest that ensemble methods provide an effective, interpretable, and computationally efficient alternative to deep learning for heart disease prediction. This study highlights the potential of ensemble learning in medical applications and provides valuable insights into optimizing classification models for structured tabular healthcare datasets.
Machine Learning-Based Obesity Classification: A Comparative Study Using Self-Reported Survey Data and Ensemble Learning Models Airlangga, Gregorius
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 1 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i1.2585

Abstract

Obesity has become one of the most pressing global health challenges of the 21st century, with its prevalence increasing at an alarming rate. Obesity is a major global health concern, contributing to an increased risk of cardiovascular disease, diabetes, and other metabolic disorders. Traditional assessment methods, such as BMI-based classification, often fail to incorporate lifestyle and behavioral factors, limiting their predictive capabilities. This study explores the use of machine learning for obesity classification based on self-reported survey data collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 2111 instances with 17 attributes, covering demographic characteristics, eating habits, and physical activity levels. Eight machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors, Naïve Bayes, and AdaBoost, were evaluated using 10-fold cross-validation. Results indicate that Gradient Boosting achieved the highest accuracy of 96.49%, followed by Random Forest and SVM, demonstrating the effectiveness of ensemble learning techniques in capturing complex feature interactions. In contrast, Naïve Bayes and AdaBoost exhibited the lowest classification performance due to their strong assumptions about feature independence and sensitivity to noisy data. The findings highlight the potential of machine learning in obesity classification and underscore the need for advanced predictive models to enhance public health monitoring and intervention strategies.
Comparative Analysis of Machine Learning Algorithms for Detecting Fake News: Efficacy and Accuracy in the Modern Information Ecosystem Airlangga, Gregorius
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3466

Abstract

In an era where the spread of fake news poses a significant threat to the integrity of the information landscape, the need for effective detection tools is paramount. This study evaluates the efficacy of three machine learning algorithms—Multinomial Naive Bayes, Passive Aggressive Classifier, and Logistic Regression—in distinguishing fake news from genuine articles. Leveraging a balanced dataset, meticulously processed and vectorized through Term Frequency-Inverse Document Frequency (TF-IDF), we subjected each algorithm to a rigorous classification process. The algorithms were evaluated on metrics such as precision, recall, and F1-score, with the Passive Aggressive Classifier outperforming others, achieving a remarkable 0.99 in both precision and recall. Logistic Regression followed with an accuracy of 0.98, while Multinomial Naive Bayes displayed robust recall at 1.00 but lower precision at 0.91, resulting in an accuracy of 0.95. These metrics underscored the nuanced capabilities of each algorithm in correctly identifying fake and real news, with the Passive Aggressive Classifier demonstrating superior balance in performance. The study's findings highlight the potential of employing machine learning techniques in the fight against fake news, with the Passive Aggressive Classifier showing promise due to its high accuracy and balanced precision-recall trade-off. These insights contribute to the ongoing efforts in digital media to develop advanced, ethical, and accurate tools for maintaining information veracity. Future research should continue to refine these models, ensuring their applicability in diverse and evolving news ecosystems.
Analysis of Machine Learning Classifiers for Speaker Identification: A Study on SVM, Random Forest, KNN, and Decision Tree Airlangga, Gregorius
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3487

Abstract

This study investigates the performance of machine learning classifiers in the domain of speaker identification, a pivotal component of modern digital security systems. With the burgeoning integration of voice-activated interfaces in technology, the demand for accurate and reliable speaker identification is paramount. This research provides a comprehensive comparison of four widely used classifiers: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT). Utilizing the LibriSpeech dataset, known for its diversity of speakers and recording conditions, we extracted Mel-frequency cepstral coefficients (MFCCs) to serve as features for training and evaluating the classifiers. Each model's performance was assessed based on precision, recall, F1-score, and accuracy. The results revealed that RF outperformed all other classifiers, achieving near-perfect metrics, indicative of its robustness and generalizability for speaker identification tasks. KNN also demonstrated high performance, suggesting its suitability for applications where rapid execution and interpretability are critical. Conversely, SVM and DT, while yielding moderate and lower performances respectively, highlighted the necessity for further optimization. These findings underscore the effectiveness of ensemble and distance-based classifiers in handling complex patterns for speaker differentiation. The study not only guides the selection of appropriate classifiers for speaker identification but also sets the stage for future research, which could explore hybrid models and the impact of dataset variability on performance. The insights from this analysis contribute significantly to the field, providing a benchmark for developing advanced speaker identification systems
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 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, Stephen Aprius Sugianto, Lai Ferry Suni, Eugenius Kau