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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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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.
A Comparative Analysis of Machine Learning Models for Predicting Student Performance: Evaluating the Impact of Stacking and Traditional Methods Airlangga, Gregorius
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4669

Abstract

This study investigates the application of machine learning models to predict student performance using socio-economic, demographic, and academic factors. Various models were developed and evaluated, including Linear Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, Support Vector Regressor, and a Stacking Regressor. The models were assessed using key evaluation metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (????2), Mean Squared Log Error (MSLE), and Mean Absolute Percentage Error (MAPE). The Support Vector Regressor demonstrated the best overall performance, with an MAE of 4.3091, RMSE of 5.4110, and an ????2 of 0.8685, surpassing even the more complex ensemble models. Similarly, Linear Regression achieved strong results, with an MAE of 4.3154 and ????2 of 0.8685. In contrast, the Stacking Regressor, while effective, did not significantly outperform its base models, achieving an MAE of 4.5340 and ????2 of 0.8563, highlighting that greater model complexity does not necessarily lead to better predictive power. The analysis also revealed that MAPE was highly sensitive to outliers in the dataset, indicating the need for robust data preprocessing to handle extreme values. These results suggest that, in educational data mining, simpler models can often match or exceed the performance of more complex methods. Future research should investigate advanced ensembling strategies and feature engineering techniques to further enhance the accuracy and reliability of student performance predictions.
Spam Detection on YouTube Comments Using Advanced Machine Learning Models: A Comparative Study Airlangga, Gregorius
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4670

Abstract

The exponential growth of user-generated content on platforms like YouTube has led to an increase in spam comments, which negatively affect the user experience and content moderation efforts. This research presents a comprehensive comparative study of various machine learning models for detecting spam comments on YouTube. The study evaluates a range of traditional and ensemble models, including Linear Support Vector Classifier (LinearSVC), RandomForest, LightGBM, XGBoost, and a VotingClassifier, with the goal of identifying the most effective approach for automated spam detection. The dataset consists of labeled YouTube comments, and text preprocessing was performed using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. Each model was trained and evaluated using a stratified 10-fold cross-validation to ensure robustness and generalizability. LinearSVC outperformed all other models, achieving an accuracy of 95.33% and an F1-score of 95.32%. The model demonstrated superior precision (95.46%) and recall (95.33%), making it highly effective in distinguishing between spam and legitimate comments. The results highlight the potential of LinearSVC for real-time spam detection systems, offering a reliable balance between accuracy and computational efficiency. Furthermore, the study suggests that while ensemble models like RandomForest and VotingClassifier performed well, they did not surpass the simpler LinearSVC model in this context. Future work will explore the incorporation of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to capture more complex patterns and further enhance spam detection accuracy on social media platforms like YouTube.
Machine Learning-Based GPS Spoofing Detection in UAV Networks: A Comparative Analysis of Anomaly Detection Models Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.7033

Abstract

The increasing reliance on Global Positioning System (GPS) technology in Unmanned Aerial Vehicles (UAVs) has exposed them to cybersecurity threats, particularly GPS spoofing attacks that manipulate location data. This study explores the effectiveness of various machine learning-based approaches in detecting GPS spoofing in UAV communication networks. Supervised classification models, unsupervised anomaly detection techniques, and deep learning-based autoencoders are evaluated to determine their capability in identifying spoofed signals. The dataset used for training and testing contains multi-dimensional UAV network parameters with labeled GPS spoofing instances. Experimental results indicate that traditional anomaly detection models, such as Isolation Forest, One-Class SVM, and Local Outlier Factor, struggle with detection accuracy and exhibit high false-positive rates. The autoencoder-based approach achieves the highest accuracy (91.20%) but has poor precision (3.97%) and recall (4.73%), highlighting limitations in threshold selection and anomaly classification. Computational complexity analysis reveals that deep learning models, despite their accuracy advantages, require significant computational resources, making them less feasible for real-time UAV applications. This study identifies critical challenges in GPS spoofing detection, including dataset bias, environmental variability, and model hyperparameter sensitivity.
Deep Learning-Based Fetal Health Classification: A Comparative Analysis of Convolutional and Recurrent Neural Networks Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.7056

Abstract

Fetal health monitoring plays a crucial role in prenatal care, enabling early detection of complications that may impact pregnancy outcomes. Traditional methods, including cardiotocography (CTG), rely on expert interpretation, which can introduce variability and potential misdiagnoses. In this study, deep learning techniques are employed to classify fetal health conditions based on CTG data. A comparative analysis is conducted on six architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Attention-based LSTM. The models are evaluated using accuracy, precision, recall, and F1-score under a 10-fold cross-validation framework. Results indicate that CNN outperforms all other models, achieving an accuracy of 97.18% due to its hierarchical feature extraction capabilities. GRU demonstrates competitive performance with an F1-score of 95.50% while maintaining computational efficiency. The study further includes a complexity analysis, revealing that recurrent models, particularly BiLSTM and Attention-LSTM, introduce significant computational overhead without yielding substantial performance gains. Potential threats to validity, including dataset bias and overfitting, are analyzed to ensure robust findings. The insights gained from this research highlight the advantages of CNN-based architectures in automated fetal health assessment and suggest future work integrating hybrid models and explainable AI techniques. These findings contribute to advancing AI-driven fetal monitoring systems, aiding clinical decision-making, and improving perinatal care.
Predicting Student Performance Using Deep Learning Models: A Comparative Study of MLP, CNN, BiLSTM, and LSTM with Attention Airlangga, Gregorius
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1668

Abstract

This study aims to predict student performance using deep learning models, including Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Long Short-Term Memory with Attention (LSTM with Attention). The dataset comprises student demographic and educational factors, and the models are evaluated using metrics such as MAE, RMSE, R², MSLE, and MAPE. The results show that the CNN model outperforms other models, achieving the highest accuracy in predicting student test scores. The MLP model also performs well, while the BiLSTM and LSTM with Attention models exhibit lower predictive performance. High MAPE values across models suggest a need for alternative metrics in future research. This study highlights the importance of selecting suitable model architectures for predictive tasks in education, emphasizing the effectiveness of convolutional layers in capturing complex patterns.
Spam Detection in YouTube Comments Using Deep Learning Models: A Comparative Study of MLP, CNN, LSTM, BiLSTM, GRU, and Attention Mechanisms Airlangga, Gregorius
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1671

Abstract

This study explores the effectiveness of various deep learning models for detecting spam in YouTube comments. Six models were evaluated: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Attention mechanisms. The dataset consists of 1,956 real comments extracted from popular YouTube videos, representing both spam and legitimate messages. The preprocessing phase involved tokenization and padding of text sequences to prepare them for model input. Results reveal that the LSTM model achieved the highest test accuracy of 95.65%, outperforming other models by capturing sequential dependencies and context within comments. The CNN model also demonstrated high accuracy, underscoring the importance of local pattern recognition in text classification. While BiLSTM and Attention models offered comparable performance, their marginal improvement over LSTM indicates that sequential modeling plays a crucial role in this task. The GRU model, despite being computationally efficient, showed slightly lower accuracy compared to LSTM and BiLSTM. The MLP model, serving as a baseline, exhibited limited performance, emphasizing the need for advanced architectures in spam detection. These findings suggest that combining sequential modeling with local feature extraction could lead to more robust spam detection systems. 
Comparative Study of Machine Learning Models for Temperature Prediction: Analyzing Accuracy, Stability, and Generalization Airlangga, Gregorius
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7114

Abstract

Accurate temperature prediction is crucial for climate monitoring, energy management, and disaster preparedness. This study provides a comparative analysis of various machine learning models, including Random Forest, Gradient Boosting, Histogram-Based Gradient Boosting, XGBoost, Support Vector Regression (SVR), Ridge Regression, and Lasso Regression, to evaluate their predictive accuracy, stability, and generalization capability. The models are assessed using five-fold cross-validation, with the R² metric as the primary evaluation criterion. The results indicate that Random Forest achieves the highest accuracy, with an R² mean of 0.999994, demonstrating its strong ability to model temperature variations. Ridge Regression unexpectedly performs at a similar level, suggesting that the dataset contains strong linear dependencies. Gradient Boosting, Histogram-Based Gradient Boosting, and XGBoost also achieve high accuracy, confirming their effectiveness in capturing complex relationships between meteorological parameters. SVR, while effective, exhibits higher variance, indicating that it may require further tuning for improved consistency. Lasso Regression, with an R² mean of 0.9783, shows the lowest accuracy, confirming that linear models are less suitable for complex meteorological predictions. These findings highlight the superiority of ensemble-based methods in temperature forecasting, reinforcing their stability and adaptability. Future research should explore hybrid models that integrate ensemble techniques with feature engineering optimizations to further enhance predictive performance. This study contributes to the ongoing development of machine learning applications in meteorology, offering insights into model selection for climate-related forecasting tasks.
Hybrid Machine Learning Approaches for Atmospheric CO₂ Prediction: Evaluating Regression and Ensemble Models with Advanced Feature Engineering Airlangga, Gregorius
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7121

Abstract

The accurate prediction of atmospheric CO₂ concentrations is essential for understanding climate change dynamics and developing effective environmental policies. This study evaluates the predictive capabilities of various machine learning models, including ensemble-based regressors such as Random Forest, Gradient Boosting, and XGBoost, alongside traditional regression models such as Support Vector Regression (SVR), Ridge, and Lasso regression. The dataset, derived from meteorological observations, was preprocessed using multiple feature scaling techniques, including StandardScaler, MinMaxScaler, and RobustScaler, followed by feature engineering techniques such as polynomial transformation and Principal Component Analysis (PCA) to enhance predictive accuracy. Model performance was assessed using the coefficient of determination (R²) and cross-validation techniques. The results indicate that tree-based models, including Random Forest and XGBoost, struggled to generalize well, exhibiting negative R² values due to overfitting and an inability to capture the temporal dependencies in CO₂ variations. SVR emerged as the best-performing model, though its predictive power remained limited. Computational complexity analysis revealed that tree-based methods incurred high processing costs, while linear models such as Ridge and Lasso demonstrated lower complexity but failed to capture non-linear dependencies. The study highlights the challenges of CO₂ prediction using conventional machine learning techniques and underscores the need for advanced deep learning approaches, such as hybrid Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, to better capture spatial and temporal dependencies. Future research should explore integrating external environmental factors and leveraging deep learning architectures to improve predictive performance.
Evaluating the Effectiveness of Machine Learning Models for Cyberattack Detection: A Study on Model Generalization and Dataset Imbalance Airlangga, Gregorius
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6089

Abstract

In today's rapidly evolving digital landscape, detecting and preventing cyberattacks has become crucial for securing networks and data. This study evaluates the performance of several machine learning models, including RandomForest, GradientBoosting, XGBoost, LightGBM, CatBoost, Support Vector Classifier (SVC), Logistic Regression, and an ensemble Voting Classifier, in detecting and classifying cyberattacks. The models were tested on a real-world cybersecurity dataset with significant class imbalance, where benign traffic vastly outnumbers malicious attacks. Results showed that while some models, such as RandomForest and the Voting Classifier, achieved high training accuracy, they suffered from overfitting, with test accuracies not exceeding 34%. Boosting models like XGBoost and LightGBM exhibited better generalization than RandomForest but still struggled to handle the dataset complexity. The primary limitations of this study include the dataset's imbalance, the high dimensionality of the features, and the models’ tendency to overfit. These challenges highlight the need for more robust data preprocessing techniques, hyperparameter tuning, and exploration of advanced models, such as deep learning architectures, for future work. The findings provide insights into the challenges of using machine learning for cybersecurity attack detection and point toward future directions for improving model performance in real-world settings.
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