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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
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

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Journal : journal of computer system and informatics josyc

 1 
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.
Stress Detection Using Hybrid Deep Learning Models with Attention Mechanisms: A Comparative Study of CNN-LSTM, CNN-GRU, and Ensemble Approaches Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6284

Abstract

Accurate and reliable stress detection remains a critical challenge in health monitoring due to the multifaceted nature of stress and the difficulty in capturing its temporal and spatial characteristics from physiological data. Existing methods often lack the ability to effectively model these dependencies, leading to suboptimal performance and limited interpretability, which hinder their application in real-world scenarios such as wearable devices and mobile health systems. This study addresses these limitations by investigating hybrid deep learning models with attention mechanisms, specifically focusing on CNN-LSTM, CNN-GRU, and CNN-BiLSTM architectures and their ensemble. Leveraging the complementary strengths of convolutional and recurrent layers, these models aim to capture both spatial and temporal dependencies in stress-related data, while attention layers enhance interpretability by prioritizing relevant features. Experimental results reveal that the CNN-LSTM with Attention model achieved the best performance, with the lowest Mean Squared Error (MSE) and Mean Absolute Error (MAE), demonstrating its suitability for complex stress prediction tasks. The CNN-GRU model also performed well, offering a balance between computational efficiency and accuracy, while the CNN-BiLSTM model showed limitations, suggesting that additional model complexity may lead to overfitting. The ensemble model, combining predictions from all three architectures, delivered stable performance across metrics, underscoring the value of ensemble approaches in improving robustness and mitigating model-specific biases. These findings have significant implications for practical applications, such as wearable devices and mobile health systems, where accurate, interpretable, and reliable stress monitoring is essential for timely interventions. Future work should focus on optimizing these models for real-time deployment, exploring adaptive learning for personalized stress detection, and validating across diverse datasets to enhance generalizability. This research highlights the importance of hybrid architectures and attention mechanisms in addressing the challenges of stress detection, paving the way for responsive and user-centered health monitoring systems.
A Hybrid Machine Learning Framework for Enhanced Tsunami Prediction Using Ensemble Models and Neural Networks Airlangga, Gregorius
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6291

Abstract

Tsunami prediction is a critical task for mitigating risks associated with natural disasters, yet achieving accurate and reliable predictions remains a significant challenge due to the inherent complexity and uncertainty in earthquake-related data. Traditional predictive models often struggle to capture the intricate relationships between earthquake features, such as magnitude, latitude, longitude, depth, and instrumental intensities, leading to suboptimal performance and unreliable predictions. To address these challenges, this research proposes a hybrid machine learning framework that integrates ensemble models and neural networks to enhance both accuracy and robustness in tsunami prediction. The dataset undergoes rigorous preprocessing, including the removal of missing values, normalization, and shuffling, to improve data quality. The framework employs a diverse set of ensemble models such as Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost alongside a neural network with three hidden layers for predictive modeling. Predictions from these models are aggregated into meta-features and passed to a logistic regression meta-classifier for final decision-making. Using ten-fold stratified cross-validation, the framework is evaluated on key metrics, including precision, recall, F1-Score, accuracy, and ROC-AUC. Results demonstrate that the hybrid model significantly outperforms individual models, effectively addressing the challenges of low accuracy and instability in traditional approaches. By leveraging the complementary strengths of ensemble models and neural networks, the proposed framework offers a scalable and adaptable solution for tsunami prediction, contributing to enhanced disaster preparedness and risk mitigation strategies.
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.
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 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