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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science International Journal Software Engineering and Computer Science (IJSECS)
Lubis, Ahmadi Irmansyah
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Computer Science & IT

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Analysis of docker container Implementation in SIEM infrastructure Ardi, Noper; Lubis, Ahmadi Irmansyah; Ikhwan Ash Shafa Arrafi
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9476

Abstract

It is known that configuring system information and event management (SIEM) infrastructure using conventional virtualization still provides essential functions. However, if a problem occurs such as a configuration error during the staging process or application service failure, the recovery process from the error requires quite a long time. This research aims to explore and analyze the implementation of container technology in the SIEM Infrastructure using the Wazuh platform. The analysis focuses on a Docker-based architecture running Wazuh's core components: the wazuh-indexer, wazuh-manager, and wazuh-dashboard, each in its own container. This approach is evaluated to see how containerization affects SIEM effectiveness and efficiency, particularly in resource utilization and fault recovery. Performance testing carried out on systems using Docker Containers shows lower Memory and CPU usage compared to Conventional Virtualization. The results demonstrate that Docker not only enhances resource efficiency but also improves system resilience, directly impacting SIEM operational functionality.
Aircraft Image Classification on a Small-Scale Dataset using MobileNetV2 with Grad-CAM as Explainable AI Lestari, Susi; Dzulfiqar, Mohamad Alif; Lubis, Ahmadi Irmansyah; Nova, Muhammad Andi; Zaimah, Zaimah; Mulyadi, Mulyadi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10546

Abstract

This study explores aircraft image classification using MobileNetV2 combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability. A dataset of 1,500 balanced images—helicopters, propeller aircraft, and jets—was split into training, validation, and testing sets with data augmentation to reduce overfitting. Transfer learning with pre-trained MobileNetV2 achieved an accuracy of 87.56%, with macro-average precision and recall of 85.76% and 87.69%. Grad-CAM visualizations confirmed that correct predictions relied on distinctive features such as rotor blades, propellers, and engines, while misclassifications often stemmed from background distractions or less discriminative areas. These findings demonstrate the potential of lightweight architectures for small-scale datasets and highlight the value of Explainable AI in validating deep learning models. The study provides a practical reference for educational contexts and offers directions for future work with larger datasets.
Co-Authors Ardi, Noper Dzulfiqar, Mohamad Alif Ginting, Erika Fahmi Ikhwan Ash Shafa Arrafi Judijanto, Loso Legito Lumban Gaol, Nur Yanti Mulyadi Mulyadi Nova, Muhammad Andi Rini Nuraini, Rini Rudy Chandra Siregar, Siti Julianita SUSI LESTARI Zaimah, Zaimah