Article Per Year (5 Year)
p-Index From 2021 - 2026
13.361
P-Index
This Author published in this journals
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
Universitas Katolik Indonesia Atma Jaya
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

Published : 113 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 113 Documents
Search

 1   2   3   4   5 
Comparative Analysis of Machine Learning Models for Chronic Disease Indicator Classification Using U.S. Chronic Disease Indicators Dataset Airlangga, Gregorius
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1403

Abstract

The prevalence of chronic diseases poses significant challenges to public health systems worldwide. This study evaluates the performance of four machine learning models—Gradient Boosting Classifier, Support Vector Machine (SVM), Logistic Regression, and Random Forest—in classifying chronic disease indicators using the U.S. Chronic Disease Indicators (CDI) dataset. The models were assessed based on accuracy, precision, recall, F1 score, classification report, and confusion matrix to determine their effectiveness. The Gradient Boosting Classifier outperformed other models with an accuracy of 64.36%, precision of 63.72%, recall of 64.36%, and F1 score of 63.88%. While SVM and Random Forest demonstrated moderate performance, Logistic Regression served as a baseline for comparison. The study highlights the Gradient Boosting Classifier's superiority in handling the complexities of the CDI dataset, suggesting its potential for improving chronic disease prediction and management. Future research should focus on refining these models, addressing class imbalances, and incorporating domain knowledge to enhance interpretability and applicability in real-world scenarios.
Comparative Analysis of Neural Network Architectures for Predicting Chronic Disease Indicators Using CDC’s Chronic Disease Indicators Dataset Airlangga, Gregorius
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1406

Abstract

This research evaluates the performance of three machine learning models—Neural Network (NN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) units—in predicting chronic disease indicators using the CDC's Chronic Disease Indicators (CDI) dataset. The study employs a comprehensive preprocessing pipeline and 5-fold cross-validation to ensure robustness and generalizability of the results. The CNN model outperformed both the NN and RNN models across all key performance metrics, achieving an accuracy of 0.6303, precision of 0.6445, recall of 0.6303, and F1 score of 0.5950. The superior performance of the CNN is attributed to its ability to capture spatial hierarchies and interactions within the structured dataset. The findings underscore the importance of selecting appropriate machine learning architectures based on the data characteristics. This research provides valuable insights for public health officials and policymakers to enhance chronic disease monitoring, early detection, and intervention strategies. Future work will explore hybrid models and advanced techniques to further improve predictive performance. This study highlights the potential of CNNs in public health informatics and sets a foundation for further research in this domain
Synergistic Machine Learning: Enhancing Diabetes Prediction with Hybrid Deep Learning and Ensemble Models Airlangga, Gregorius
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.457

Abstract

Diabetes, a growing global health concern, necessitates improved predictive strategies for early and accurate detection. This study evaluates the efficacy of various machine learning and deep learning models in predicting the onset of diabetes, employing a comprehensive dataset that includes clinical and demographic variables. Traditional machine learning models such as Decision Trees, Random Forest, KNN, and XGBoost provided foundational insights, with ensemble methods showing superior performance. Furthermore, we explored the potential of deep learning by analyzing a Simple Dense Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). While these individual models yielded valuable findings, particularly in identifying true positive cases, they did not surpass the ensemble techniques in overall accuracy. The pinnacle of our research was the development of a Deep Learning Meta Learner that combined Random Forest and Gradient Boosting predictions, achieving near-perfect classification metrics, and underscoring the strength of model integration. Our findings advocate for a hybrid predictive approach that merges the nuanced feature detection of deep learning with the robust pattern recognition of ensemble models, providing an impactful direction for future diabetes prediction research. This study contributes to the advancement of medical informatics and aims to support healthcare professionals in delivering proactive and personalized patient care.
Enhancing Concrete Compressive Strength Prediction with Deep Learning: A Comparative Analysis of Model Architectures Airlangga, Gregorius
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.459

Abstract

The imperative to predict concrete compressive strength accurately is a crucial aspect of modern civil engineering, with significant implications for the safety and cost-effectiveness of construction projects. This research explores the application of deep learning techniques to enhance predictive accuracy in this domain. We conducted a comprehensive comparative analysis of five machine learning models: a Basic neural network model, a Dropout model, a Batch Normalization model, a Deep Dense Neural Network (Deep DNN), and a Convolutional Neural Network (CNN). Utilizing a dataset reflective of various concrete mixtures and their corresponding compressive strengths, each model underwent rigorous evaluation through a five-fold cross-validation scheme. Performance metrics, including Mean Squared Error (MSE) and R-Squared (R²), were computed to assess each model's predictive capabilities. The results indicated that models employing batch normalization and deeper architectures provided superior predictive performance, suggesting that these features are instrumental in understanding the complex relationships between the components of concrete mixtures. The Batch Normalization and Deep DNN models demonstrated remarkable accuracy and consistency, surpassing traditional and CNN models. This study not only enhances the current understanding of material property prediction through machine learning but also paves the way for the development of more efficient and robust predictive tools in civil engineering. The findings underscore the transformative potential of deep learning in material science, emphasizing its ability to deliver nuanced and precise predictions for critical engineering properties.
Comparative Analysis of Machine Learning Models for Classifying Human DNA Sequences: Performance Metrics and Strategic Recommendations Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5168

Abstract

This study presents a comprehensive evaluation of seven machine learning models applied to the classification of human DNA sequences, highlighting their performance and potential applications in genomics. We explored Logistic Regression, Support Vector Machines (SVM), Random Forest, Decision Trees, Gradient Boosting, Naive Bayes, and XGBoost, using a 5-fold StratifiedKFold cross-validation method to ensure robustness and reliability in our findings. Naive Bayes demonstrated exceptional performance with near-perfect accuracy, precision, recall, and F1 scores, suggesting its suitability for rapid and efficient genomic classification. Logistic Regression also showed high efficacy, proving effective even in multi-class classifications of complex genetic data. Conversely, Decision Trees and SVM struggled with overfitting and computational efficiency, respectively, indicating the need for careful parameter tuning and optimization in practical applications. The study addresses these challenges and proposes strategies for enhancing model robustness and computational efficiency, such as advanced regularization techniques and hybrid modeling approaches. These insights not only aid in selecting appropriate models for specific genomic tasks but also pave the way for future research into integrating machine learning with genomic science to advance personalized medicine and genetic research. The findings encourage ongoing refinement of these models to unlock further potential in genomic applications.
Comparative Analysis of Deep Learning Architectures for DNA Sequence Classification: Performance Evaluation and Model Insights Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5170

Abstract

The classification of DNA sequences using deep learning models offers promising avenues for advancements in genomics and personalized medicine. This study provides a comprehensive evaluation of several deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Bidirectional LSTMs (BiLSTMs), and hybrid models combining CNNs with various recurrent networks, to classify human DNA sequences into functional categories. We employed a dataset of approximately 100,000 labeled sequences, ensuring a balanced representation across seven distinct classes to facilitate a fair comparison of model performance. Each model was assessed based on accuracy, precision, recall, F1 score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The CNN model demonstrated superior accuracy (74.86%) and the highest AUC (94.64%), indicating its effectiveness in capturing spatial patterns within sequences. LSTM and GRU models showed commendable performance, particularly in balancing precision and recall, suggesting their capability in managing sequential dependencies. However, hybrid models did not perform as expected, showing lower overall metrics, which highlighted challenges in model integration and complexity management. The findings suggest that while CNNs excel in feature extraction, sequence-based models like LSTMs and GRUs provide valuable capabilities in capturing long-range dependencies, essential for comprehensive genomic analysis. The study underscores the need for optimized hybrid models and further research into model robustness and generalizability.
A Hybrid Ensemble Approach for Enhanced Fraud Detection: Leveraging Stacking Classifiers to Improve Accuracy in Financial Transaction Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5840

Abstract

Fraud detection in financial transactions presents a significant challenge due to the evolving tactics of fraudsters and the inherent imbalance in datasets, where fraudulent activities are rare compared to legitimate transactions. This study proposes a Hybrid Model utilizing a stacking ensemble technique that combines multiple machines learning algorithms, including Random Forest, Gradient Boosting, SVM, LightGBM, and XGBoost, to enhance the accuracy of fraud detection systems. The Hybrid Model is evaluated against traditional machine learning models using a comprehensive cross-validation approach, with results indicating a near-perfect accuracy of 99.99%, outperforming all individual models. The study also examines the trade-offs associated with the Hybrid Model, including increased computational demands and reduced interpretability, highlighting the need for careful consideration when deploying such models in real-world scenarios. Despite these challenges, the Hybrid Model's ability to significantly reduce both false positives and false negatives makes it a powerful tool for financial institutions aiming to mitigate the risks associated with fraudulent activities. In conclusion, the findings demonstrate the effectiveness of hybrid ensemble methods in fraud detection, providing a robust solution that balances the complexities of real-world applications with the need for high accuracy. The research underscores the potential of advanced machine learning techniques in enhancing the security and reliability of financial transactions, offering valuable insights for the development of future fraud detection systems.
Anemia Classification Using Hybrid Machine Learning Models: A Comparative Study of Ensemble Techniques on CBC Data Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5848

Abstract

Anemia is a prevalent and potentially serious medical condition characterized by a deficiency in the number or quality of red blood cells. Accurate classification of anemia types is crucial for ensuring appropriate treatment, as different types of anemia require distinct therapeutic approaches. However, the classification of anemia presents specific challenges due to the complexity of the condition, the variability in CBC data, and the need to differentiate between multiple anemia types that may present with overlapping symptoms. In this study, we explore the application of hybrid machine learning models to classify anemia types using Complete Blood Count (CBC) data. We evaluated the performance of various models, including DecisionTree, RandomForest, XGBoost, LightGBM, CatBoost, and ensemble methods such as Stacking and Voting. The ensemble models, particularly Stacking and Voting, demonstrated superior performance with balanced accuracy reaching 0.9976 and F1 scores of 0.9964, significantly outperforming individual classifiers. These results underscore the efficacy of ensemble techniques in handling the complex and imbalanced datasets commonly encountered in medical diagnostics. Despite their high accuracy, we identified challenges related to model interpretability, computational demands, and data quality. The complexity and resource requirements of these models may limit their practical application in resource-constrained environments. This study provides a comprehensive analysis of the trade-offs between model complexity, accuracy, and interpretability, offering valuable insights for the deployment of machine learning models in clinical settings. Our findings highlight the potential of hybrid models to improve anemia diagnosis, suggesting their integration into healthcare systems could enhance diagnostic accuracy and patient outcomes. Future work will focus on expanding the dataset, refining model interpretability, and addressing ethical considerations in the use of AI in healthcare.
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.
Analysis and Comparison of Machine Learning Techniques for DDoS Attack Classification in Network Environments Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 1 (March 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i1.795

Abstract

This research presents a comparative analysis of machine learning techniques for classifying Distributed Denial of Service (DDoS) attacks within network traffic. We evaluated the performance of three algorithms: Logistic Regression, Decision Tree, and Random Forest, including their scaled-feature counterparts. The study utilized a robust methodology incorporating advanced data preprocessing, feature engineering, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The models were rigorously tested using a cross-validation framework, assessing their accuracy, precision, recall, and F1 score. Results indicated that the Random Forest algorithm outperformed the others, demonstrating superior predictive accuracy and consistency, albeit with higher computational costs. Logistic Regression, when feature-scaled, showed significant improvement in performance, highlighting the importance of data normalization in models sensitive to feature scaling. Decision Trees provided a quick and interpretable model, though slightly less accurate than the Random Forest. The research findings highlight the trade-offs between predictive performance and computational efficiency in selecting machine learning models for cybersecurity applications. The study contributes to the cybersecurity domain by elucidating the efficacy of ensemble techniques in DDoS attack classification and underscores the potential for model improvement through scaling and data balancing.
Co-Authors Afandi Nur Aziz Thohari, Afandi Nur Aziz Alamsyah - Amalia, Andika Elok Christanto, Henoch Juli Denny Jean Cross Sihombing Eugenius Kau Suni Henoch Juli Christanto Julius Bata Khairul Huda Kristian, Yoel Lenson, Abraham K. S. Lim, Bobi Hartanto Pramudita Makin, See Jong Nugroho, Oskar Ika Adi Oskar Ika Adi Nugroho 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 Wicaksana, Hilman Singgih Widodo Widjaja Basuki