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A Comparative Analysis of Machine Learning Models for Obesity Prediction Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 1 (March 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i1.1089

Obesity is a global health challenge with significant implications for public health systems and individual well-being. Predictive modeling using machine learning (ML) offers a powerful approach to identify individuals at risk of obesity and inform early intervention strategies. This study evaluates the performance of ten ML models, including Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, Naive Bayes, Random Forest, Gradient Boosting, AdaBoost, XGBoost, and LightGBM, in predicting obesity using a publicly available dataset. A rigorous preprocessing pipeline, incorporating missing value handling, categorical encoding, normalization, and outlier detection, was applied to ensure data quality and compatibility with ML algorithms. Performance metrics such as accuracy, precision, recall, and F1-score were evaluated using 10-fold stratified cross-validation. Among the models, LightGBM demonstrated the highest test accuracy (99.19%) and F1-score (99.20%), outperforming Gradient Boosting and Random Forest, which also showed competitive results. The study highlights the superior predictive capabilities of ensemble methods while underscoring the trade-offs between model complexity and interpretability. Logistic Regression provided a strong baseline, demonstrating the importance of preprocessing, but was outperformed by advanced ensemble techniques. This research contributes to the growing field of ML-driven healthcare solutions, offering valuable insights into the strengths and limitations of various predictive models. The findings support the integration of advanced ML techniques in public health systems and pave the way for future research on hybrid and explainable models for obesity prediction and management.

A Comparative Analysis of Deep Learning Architectures for Obesity Classification Using Structured Data Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 1 (March 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i1.1090

Obesity is a significant global health concern, necessitating accurate and efficient diagnostic tools to classify individuals based on obesity levels. This study investigates the performance of five deep learning architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) in classifying obesity levels using structured data. The dataset comprises clinical, demographic, and lifestyle features, and is preprocessed through normalization, label encoding, and Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Each model was evaluated using accuracy, precision, recall, and F1-score metrics under stratified 10-fold cross-validation. The results indicate that MLP achieved the highest performance across all metrics, with an accuracy of 99.05%, followed closely by CNN at 98.77%. Sequential models, including LSTM, GRU, and BiLSTM, exhibited comparatively lower performance, achieving accuracies of 83.80%, 86.59%, and 86.78%, respectively. The superior performance of MLP and CNN underscores their suitability for structured datasets with static features, while the sequential models struggled due to the lack of temporal dependencies in the data. This study highlights the importance of aligning model architecture with dataset characteristics for optimal performance. The findings suggest that MLP and CNN are effective choices for obesity classification tasks, providing robust and computationally efficient solutions. Future work could explore hybrid models and incorporate temporal features to enhance the performance of sequential architecture.

A Comparative Analysis of Machine Learning Models for Obesity Prediction Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 1 (March 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i1.1089

Obesity is a global health challenge with significant implications for public health systems and individual well-being. Predictive modeling using machine learning (ML) offers a powerful approach to identify individuals at risk of obesity and inform early intervention strategies. This study evaluates the performance of ten ML models, including Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, Naive Bayes, Random Forest, Gradient Boosting, AdaBoost, XGBoost, and LightGBM, in predicting obesity using a publicly available dataset. A rigorous preprocessing pipeline, incorporating missing value handling, categorical encoding, normalization, and outlier detection, was applied to ensure data quality and compatibility with ML algorithms. Performance metrics such as accuracy, precision, recall, and F1-score were evaluated using 10-fold stratified cross-validation. Among the models, LightGBM demonstrated the highest test accuracy (99.19%) and F1-score (99.20%), outperforming Gradient Boosting and Random Forest, which also showed competitive results. The study highlights the superior predictive capabilities of ensemble methods while underscoring the trade-offs between model complexity and interpretability. Logistic Regression provided a strong baseline, demonstrating the importance of preprocessing, but was outperformed by advanced ensemble techniques. This research contributes to the growing field of ML-driven healthcare solutions, offering valuable insights into the strengths and limitations of various predictive models. The findings support the integration of advanced ML techniques in public health systems and pave the way for future research on hybrid and explainable models for obesity prediction and management.

A Comparative Analysis of Deep Learning Architectures for Obesity Classification Using Structured Data Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 1 (March 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i1.1090

Obesity is a significant global health concern, necessitating accurate and efficient diagnostic tools to classify individuals based on obesity levels. This study investigates the performance of five deep learning architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) in classifying obesity levels using structured data. The dataset comprises clinical, demographic, and lifestyle features, and is preprocessed through normalization, label encoding, and Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Each model was evaluated using accuracy, precision, recall, and F1-score metrics under stratified 10-fold cross-validation. The results indicate that MLP achieved the highest performance across all metrics, with an accuracy of 99.05%, followed closely by CNN at 98.77%. Sequential models, including LSTM, GRU, and BiLSTM, exhibited comparatively lower performance, achieving accuracies of 83.80%, 86.59%, and 86.78%, respectively. The superior performance of MLP and CNN underscores their suitability for structured datasets with static features, while the sequential models struggled due to the lack of temporal dependencies in the data. This study highlights the importance of aligning model architecture with dataset characteristics for optimal performance. The findings suggest that MLP and CNN are effective choices for obesity classification tasks, providing robust and computationally efficient solutions. Future work could explore hybrid models and incorporate temporal features to enhance the performance of sequential architecture.

Detection of DDoS Attacks in UAV Communication Networks Using Machine Learning Models Airlangga, Gregorius
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.v10i1.882

The increasing adoption of unmanned aerial vehicle (UAV) communication networks has introduced new cybersecurity challenges, particularly in detecting and mitigating distributed denial-of-service (DDoS) attacks. This study evaluates the effectiveness of multiple machine learning models, including Random Forest, Gradient Boosting, XGBoost, Logistic Regression, and Support Vector Machine (SVM), for DDoS attack detection in UAV networks. The dataset, derived from a simulated UAV communication network, incorporates key network parameters such as signal strength, packet loss rate, round-trip time, and base station load. Data preprocessing steps, including feature selection, normalization, and synthetic minority over-sampling (SMOTE), were applied to enhance model performance. Among the evaluated models, Random Forest demonstrated the highest classification accuracy with an F1-score of 0.839 and an AUC score of 0.912, outperforming other models in precision-recall trade-offs. Gradient Boosting and XGBoost exhibited moderate classification ability, whereas Logistic Regression and SVM struggled with capturing complex network patterns. The results highlight the effectiveness of ensemble learning in intrusion detection for UAV networks. This study provides valuable insights into optimizing machine learning-based intrusion detection systems and paves the way for further advancements in UAV cybersecurity. Future work will focus on integrating additional feature engineering techniques and validating models on real-time network traffic datasets.

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

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

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

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

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

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.

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