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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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Evaluating the Efficacy of Machine Learning Models in Credit Card Fraud Detection Airlangga, Gregorius
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3814

Abstract

This research evaluates the effectiveness of various machine learning models in detecting credit card fraud within a dataset comprising 555,719 transactions. The study meticulously compares traditional and advanced models, including Logistic Regression, Support Vector Machines (SVM), Random Forest, Gradient Boosting, k-Nearest Neighbors (k-NN), Naive Bayes, AdaBoost, LightGBM, XGBoost, and Multilayer Perceptrons (MLP), in terms of accuracy and reliability. Through a robust methodology involving extensive data preprocessing, feature engineering, and a 5-fold stratified cross-validation, the research identifies XGBoost as the most effective model, demonstrating a near-perfect mean accuracy of 0.9990 with minimal variability. The results emphasize the significance of model choice, data preparation, and the potential of ensemble and boosting techniques in managing the complexities of fraud detection. The findings not only contribute to the academic discourse on fraud detection but also suggest practical applications for real-world systems, aiming to enhance security measures in financial transactions. Future research directions include exploring hybrid models and adapting to evolving fraud tactics through continuous learning systems.
Comparative Analysis of Machine Learning Models for Credit Card Fraud Detection in Imbalanced Datasets Airlangga, Gregorius
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3816

Abstract

This study presents a comprehensive evaluation of various machine learning models for detecting credit card fraud, emphasizing their performance in handling highly imbalanced datasets. We focused on three models: Logistic Regression, Random Forest, and Multilayer Perceptron (MLP), using a dataset comprising 555,719 transactions, each annotated with 22 attributes. Logistic Regression served as a baseline, Random Forest was evaluated for its high accuracy and low dependency on hyperparameter tuning, and MLP was tested for its capability to identify non-linear patterns. The models were assessed using ROC AUC, Matthews Correlation Coefficient (MCC), and precision-recall curves to determine their effectiveness in distinguishing fraudulent transactions. Results indicated that the Random Forest model outperformed others with a ROC AUC of 0.9868 and an MCC of 0.6638, showing substantial superiority in managing class imbalances and complex data interactions. Logistic Regression, although useful as a benchmark, exhibited limitations with a high number of false positives. MLP showed potential but was prone to a significant false positive rate, suggesting a need for further model refinement. The findings highlight the importance of choosing appropriate models and feature engineering techniques in fraud detection systems and suggest avenues for future research in real-time model deployment and advanced algorithmic strategies
Optimizing SMS Spam Detection Using Machine Learning: A Comparative Analysis of Ensemble and Traditional Classifiers Airlangga, Gregorius
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4822

Abstract

With the rapid rise of mobile communication, Short Message Service (SMS) has become an essential platform for transmitting information. However, the growing volume of unsolicited and harmful spam messages presents significant challenges for both users and mobile network operators. This study explores the effectiveness of various machine learning models, including Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), Logistic Regression, and an Ensemble Voting Classifier, in detecting SMS spam. A dataset containing 5,572 SMS messages, labeled as either spam or ham (legitimate), was used to evaluate these models. Hyperparameter tuning was performed on each model to optimize accuracy, and the models were assessed using metrics such as precision, recall, F1-score, and accuracy. The results indicated that the SVM and Ensemble Voting Classifier achieved the highest performance, with accuracies of 0.9857 and 0.9848, respectively. Both models demonstrated superior recall for spam messages, making them highly effective for real-world spam detection systems. While Random Forest, Gradient Boosting, and AdaBoost also performed well, their slightly lower recall for spam suggests that they may misclassify some spam as legitimate messages. The study highlights the effectiveness of machine learning models in addressing the SMS spam problem, particularly when using ensemble methods. Future research should focus on addressing class imbalance and exploring deep learning approaches to further enhance model performance. These findings offer valuable insights for developing more accurate and scalable SMS spam detection systems.
A Comparative Analysis of Deep Learning Models for SMS Spam Detection: CNN-LSTM, CNN-GRU, and ResNet Approaches Airlangga, Gregorius
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4827

Abstract

Spam messages have become a growing challenge in mobile communication, threatening user security and data privacy. Traditional spam detection methods, including rule-based and machine learning techniques, are increasingly insufficient due to the evolving sophistication of spam tactics. This research evaluates the effectiveness of advanced deep learning models such as CNN-LSTM, CNN-GRU, and ResNet for SMS spam detection. The dataset used consists of diverse SMS messages labeled as either spam or legitimate (ham), ensuring broad coverage of real-world spam patterns. The study employs a robust ten-fold cross-validation approach to assess the generalization capabilities of the models, measuring performance based on accuracy, precision, recall, and F1 score. The results indicate that ResNet outperformed the other models, achieving an average accuracy of 99.08% and an F1 score of 0.9646, making it the most reliable model for spam detection. CNN-GRU demonstrated competitive performance with a balance between accuracy (98.97%) and computational efficiency, making it suitable for real-time applications. CNN-LSTM, while highly accurate (98.92%), showed a slightly lower recall compared to the other models, indicating a more cautious approach to detecting spam. These findings highlight the potential of hybrid deep learning models in addressing the complexities of SMS spam detection. Future research could focus on optimizing these models for deployment in resource-constrained environments, such as mobile devices, and further exploring the integration of residual connections for more effective spam filtering.
Comparative Analysis of Machine Learning Models for Intrusion Detection in Internet of Things Networks Using the RT-IoT2022 Dataset Airlangga, Gregorius
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1304

Abstract

This research investigates the performance of various machine learning models in developing an Intrusion Detection System (IDS) for the complex and evolving security landscape of Internet of Things (IoT) networks. Employing the RT-IoT2022 dataset, which captures a diverse array of IoT devices and attack methodologies, we meticulously evaluated four prominent models: Gradient Boosting, Random Forest, Logistic Regression, and Multi-Layer Perceptron (MLP). Our results indicate that both Gradient Boosting and Random Forest achieved perfect scores with an accuracy, precision, recall, and F1 score of 1.00, suggesting their superior ability to classify and predict security incidents within the dataset. Logistic Regression demonstrated commendable consistency with scores of 0.96 across all metrics, proposing a balance between model complexity and performance. The MLP model closely followed, with an accuracy, precision, recall, and F1 score of 0.99, highlighting its potential in capturing complex, nonlinear data relationships. These findings underscore the critical role of machine learning in fortifying IoT networks against cyber threats and the need for continuous model evaluation against real-world data. The study provides a pathway for future research to refine these IDS models for operational efficiency and sustainability in the dynamic IoT security domain. 
Advancing fake news detection: a comparative study of RNN, LSTM, and Bidirectional LSTM Architectures Airlangga, Gregorius
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.696.pp13-23

Abstract

In the era of information overload, the exponential growth of digital content has coincided with the proliferation of 'fake news,' posing a critical challenge to online information credibility. This study addresses the pressing need for robust fake news detection systems by conducting a comparative analysis of three neural network architectures: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Our primary objective is to assess their effectiveness in identifying fake news in a binary classification setting. To achieve this goal, we employed advanced neural network models and a dataset of news titles. Our applied research method included data preprocessing and the utilization of RNN, LSTM, and BiLSTM models, each tailored to handle sequential data and capture temporal dependencies. we rigorously assessed the performance of RNN, LSTM, and BiLSTM models using a range of metrics, including accuracy, precision, recall, and F1-score. To achieve a comprehensive evaluation, we divided our dataset into training and testing subsets. Specifically, we allocated 67% of the data for training purposes and the remaining 33% for testing. Our research findings reveal that all three models consistently achieved high accuracy levels, approximately 91%, with slight variations in precision and recall. Notably, the LSTM model exhibited a marginal improvement in recall, which is crucial when the consequences of missing deceptive content outweigh false alarms. Conversely, the RNN model demonstrated slightly better precision, making it suitable for applications where minimizing false positives is paramount. Surprisingly, the BiLSTM model did not significantly outperform the unidirectional models, suggesting that, for our dataset, processing information bidirectionally may not be essential. In conclusion, our study contributes valuable insights to the field of fake news detection. It underscores the significance of model selection based on specific task requirements and dataset characteristics.
Enhancing Facial Emotion Recognition on FER2013 Using Attention-based CNN and Sparsemax-Driven Class-Balanced Architectures Suwartono, Christiany; Bata, Julius Victor Manuel; Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14510

Abstract

Facial emotion recognition plays a critical role in various human–computer interaction applications, yet remains challenging due to class imbalance, label noise, and subtle inter-class visual similarities. The FER2013 dataset, containing seven emotion classes, is particularly difficult because of its low resolution and heavily skewed label distribution. This study presents a comparative investigation of advanced deep learning architectures against traditional machine-learning baselines on FER2013 to address these challenges and improve recognition performance. Two novel architectures are proposed. The first is an attention-based convolutional neural network (CNN) that integrates Mish activations and squeeze-and-excitation (SE) channel recalibration to enhance the discriminative capacity of intermediate features. The second, FastCNN-SE, is a refined extension designed for computational efficiency and minority-class robustness, incorporating Sparsemax activation, Poly-Focal loss, class-balanced reweighting, and MixUp augmentation. The research contribution is demonstrating how combining attention, sparse activations, and imbalance-aware learning improves FER performance under challenging real-world conditions. Both models were extensively evaluated: the attention-CNN under 10-fold cross-validation, achieving 0.6170 accuracy and 0.555 macro-F1, and FastCNN-SE on the held-out test set, achieving 0.5960 accuracy and 0.5138 macro-F1. These deep models significantly outperform PCA-based Logistic Regression, Linear SVC, and Random Forest baselines (≤0.37 accuracy and ≤0.29 macro-F1). We additionally justify the differing evaluation protocols by emphasizing cross-validation for architectural stability and held-out testing for generalization and note that FastCNN-SE contains ~3M parameters, enabling efficient inference. These findings demonstrate that architecture-level fusion of SE attention, Sparsemax, and Poly-Focal loss improves balanced emotion recognition, offering a strong foundation for future studies on efficient and robust affective-computing 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