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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Ferry Wahyu Wibowo
Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
Computer Science & IT

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Leveraging Vector Quantized Variational Autoencoder for Accurate Synthetic Data Generation in Multivariate Time Series Mohammad Diqi; Ema Utami; Kusrini Kusrini; Ferry Wahyu Wibowo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4514

Abstract

This study addresses the challenge of generating high-quality synthetic financial time series data, acritical issue in financial forecasting due to limited access to complete and reliable historical datasets.The aim of this research was to compare the performance of the standard Variational Autoencoder andthe Vector Quantized Variational Autoencoder (VQ-VAE) in generating synthetic multivariate time seriesdata using the Adaro Energy Indonesia stock dataset. The VQ-VAE incorporates a discrete latentspace to improve the structure and control of the data generation process, whereas the standard VAEutilizes a continuous latent space. This research method was based on the implementation of bothmodels, followed by a quantitative evaluation using statistical metrics, including mean absolute error(MAE), mean squared error (MSE), root mean squared error (RMSE), and R² score. This researchshowed that the VQ-VAE outperformed the standard VAE in replicating the statistical characteristicsof stock prices, as shown by lower error values and higher R² scores across all tested features. The discretelatent space of the VQ-VAE led to the generation of more structured and statistically consistentsynthetic data. The implications of these findings suggest that the VQ-VAE model is highly suitablefor financial forecasting applications and indicate the potential for future enhancements throughintegration with hybrid models, such as attention mechanisms or generative adversarial networks.
Operational Weakness Mapping of Machine Learning–Based IntrusionDetection Systems under Realistic Deployment Scenarios Fathoni Mahardika; Ema Utami; Kusrini; Ferry Wahyu Wibowo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.6147

Abstract

As machine learning-based intrusion detection systems increasingly support information security risk management, prior systematic literature review findings indicate that many studies still emphasize benchmark accuracy while paying limited attention to robustness, interpretability, and operational feasibility. This study aims to map the operational weaknesses of machine learning-based intrusion detection systems under realistic deployment stressors. A directed replication and scenario-based stresstesting approach was applied using four public intrusion detection datasets, namely CICIDS2017, CICIDS2018, UNSW-NB15, and RanSMAP. The data were obtained from public repositories, converted to binary labels, cleaned by removing identifiers and non-numeric attributes, imputed with median values, scaled with MinMax normalization, and split into training and testing subsets. Supervised models, including Random Forest and XGBoost, were compared with unsupervised baselines, including Isolation Forest, LOF/kNN-distance, and DBSCAN, across scenarios covering baseline benchmarking, class imbalance, telemetry degradation, drift, parameter sensitivity, and micro-batch inference. The results show that supervised models achieved near-perfect baseline performance but degraded sharply under minor Gaussian noise, with F1-score dropping to 0.16 for Random Forest and 0.41 for XGBoost. Unsupervised models showed limited detection capability and high sensitivity to parameters. Although micro-batch inference achieved high throughput, alert burden remained a practical concern. These findings demonstrate that benchmark accuracy alone is insufficient for deployment readiness and that IDS evaluation should include robustness, interpretability, and alert-management analysis.
Co-Authors Ema Utami Fathoni Mahardika Kusrini Mohammad Diqi