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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Embedded Systems, Security and Intelligent Systems Jurnal Sistem Informasi dan Teknologi Informasi
Syarwani, Andi
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Journal : Journal of Embedded Systems, Security and Intelligent Systems

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COMPASS: Comparative Evaluation of Machine Learning Algorithms for DDoS Detection Using ANOVA F-Value on AISED Dataset Hartinah; Syamsuddin, Irfan; Syarwani, Andi
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8276

Abstract

This study presents COMPASS, a comparative evaluation of ten Machine Learning algorithms for DDoS attack detection using the AISED Dataset on Cloud DDoS Attacks. Feature selection was performed using SelectKBest with ANOVA F-Value, evaluating model performance across varying feature dimensions (K = 10, 15, 20, 25). Experimental results demonstrate that ensemble-based methods, particularly Random Forest, Gradient Boosting, and AdaBoost, achieve near-theoretical maximum AUC scores (>0.998) while maintaining fast training times (<0.1 seconds). K-Nearest Neighbors (KNN) also exhibits robust performance (AUC > 0.98) with minimal computational cost. In contrast, Support Vector Machine (SVM) and Quadratic Discriminant Analysis (QDA) show relatively lower accuracy (AUC > 0.85) and suffer from high computational complexity, with SVM requiring up to 572 seconds to train at K=25. These findings highlight the critical trade-off between classification accuracy and computational efficiency in selecting optimal models for real-time DDoS detection systems. As future work, we propose deploying a lightweight version of COMPASS on edge computing devices and integrating it into federated learning frameworks to enable collaborative, privacy preserving model training.
Automated Student Activity Monitoring Based on Spatiotemporal Modeling Using MediaPipe and Long Short-Term Memory Andi Syarwani; Hartinah; Maya Itasari; Nurul Amalia Amri; Annisa Nurfadhilah; Muhdalifah Muhtar
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9371

Abstract

Computer vision-based Human Activity Recognition (HAR) systems hold significant potential for applications in educational settings, particularly for monitoring student activities in laboratories or classrooms. Activities such as typing, smartphone usage, and resting are often visually indistinguishable due to their highly similar seated postures. This study proposes a spatiotemporal modeling approach to automatically and non-invasively recognize such activities. Body poses are extracted from video streams using MediaPipe Pose and represented as sequential feature vectors, which are then analyzed using a Long Short-Term Memory (LSTM) network to capture temporal dynamics. The model is trained on video data of students performing three primary activity classes. Evaluation on validation data demonstrates a classification accuracy of 98.48%, with average precision, recall, and F1-score values of approximately 98%. However, testing on unseen videos shows a decrease in accuracy to around 65%, primarily due to misclassification in segments with minimal movement. These findings suggest that the model is sensitive to subtle pose transitions, which are common in seated activity contexts. Overall, the proposed approach demonstrates promising potential for automated student activity monitoring and provides a foundation for developing pose-based behavioral analysis systems in contextual learning environments.
Co-Authors Amiruddin, M Rudini Kurniawan Andi Asvin Mahersatillah Suradi Annisa Nurfadhilah Furqan, Andi Asyraf Hartinah Hartinah Hartinah Irfan Syamsuddin Maya Itasari Muhdalifah Muhtar Nurul Amalia Amri