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ComEngApp : Computer Engineering and Applications Journal
Published by Universitas Sriwijaya
ISSN : 22524274     EISSN : 22525459     DOI : 10.18495
Core Subject : Science, Engineering,
Computer Science & IT Engineering
ComEngApp-Journal (Collaboration between University of Sriwijaya, Kirklareli University and IAES) is an international forum for scientists and engineers involved in all aspects of computer engineering and technology to publish high quality and refereed papers. This Journal is an open access journal that provides online publication (three times a year) of articles in all areas of the subject in computer engineering and application. ComEngApp-Journal wishes to provide good chances for academic and industry professionals to discuss recent progress in various areas of computer science and computer engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 343 Documents
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Architectural Optimization Of A Lightweight Kubernetes Microservices Framework For The Toba Herbal Information System Under Extreme Resource Constraints Pandapotan Siagian
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1415

Abstract

This study designs, implements, and evaluates a lightweight, decoupled virtualization architecture utilizing K3s Kubernetes and Docker containers tailored for the Toba Herbal Information (THI). The system architecture isolates the web application logic, stateful relational databases, and high-velocity user connection sessions into three independent, resource-constrained pod tiers. Operating under a strict single-node hardware envelope of 4 CPU cores and 2 GB RAM, the framework enforces rigorous memory quotas and integrates an in-memory Redis caching buffer. Performance resiliency was rigorously evaluated via Apache JMeter against a baseline monolithic virtual machine (VM) utilizing a peak load of 1,000 concurrent healthcare users, complemented by an intentional Chaos Engineering failure injection. Empirical benchmarks demonstrate that the proposed 3-pod K3s framework successfully accommodated peak traffic with a stable 95th percentile (P_95) latency under 340ms and zero Out-Of-Memory (OOM) disruptions, safely capping RAM saturation at 87%. Conversely, the monolithic setup suffered a catastrophic kernel freeze at 400 concurrent users. Following a forced application pod termination, the K3s control plane achieved an exceptional Mean Time to Recover (MTTR) of 3.8 seconds with 100% transactional data integrity. This paper establishes a repeatable, cost-effective architectural blueprint proving that defensive microservices orchestration can successfully overcome physical hardware barriers to deliver enterprise-grade digital health platforms in resource-constrained rural regions.
Complex-Valued Representation of Relevance and Semantic Relations for Claim-Evidence Pairs with PSO Boundary Calibration Fadilah Nur Imani; Arsyelina Husni Johan
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1421

Abstract

This study constructs f(c,e), a complex-valued function in a polar form as a measure of relevance and semantic orientation between claims and evidence, where the modulus component represents semantic relevance and the angle component represents semantic orientation, which reflects the direction of the relationship between claims and evidence. The function is proven to have four mathematical properties: well-definedness, boundedness, continuity, and symmetry. Particle Swarm Optimization (PSO) is used to determine the angular boundaries that partition the semantic orientation space into support, not enough information, and refute relation zones. This approach ensures that the resulting zone boundaries reflect the characteristics of the empirical data distribution. The angular boundary calibration experiment was conducted using two datasets, namely SNLI and FEVER, and with two embedding models, namely all-MiniLM-L6-v2 and mxbai-embed-large-v1. The PSO results were evaluated using the F1-macro objective function. The obtained F1-macro ranged from 0.4105 to 0.59888, with the combination of SNLI and mxbai-embed-large-v1 producing the highest value. In this experiment, zone separation was descriptively clearer for SNLI than for FEVER, with SNLI exhibiting wider NEI zones and less overlap among relation distributions.
DDoS Detection in Smart Home IoT Networks: An Experimental Comparison of MLP and AE-MLP with Correlation-Based Feature Selection Nurul Afifah; Septiani Kusuma Ningrum; RA Siti Safa Marefa; Jihan Badiatus Shaliha; Hikmah Maharani; Ilma Sari; Andi Daniella
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1422

Abstract

As the use of the Internet of Things (IoT) increases, the number of smart homes is rapidly increasing. But the increase in the number of connected devices increases the attack surface, making them vulnerable to Distributed Denial-of-Service (DDoS) attacks. The Autoencoder-Multilayer Perceptron (AE-MLP) is a hybrid deep learning model for DDoS attack detection in the smart home context. The Multilayer Perceptron (MLP) is proposed as the baseline model. The dataset is collected from a testbed of several IoT devices deployed in smart homes. Flow based features were extracted from network traffic in PCAP format using the CICFlowMeter. Data cleaning, feature selection with the correlation matrix and Min-Max normalization were the steps of data preparation process. We tried different data partition, optimizer, learning rate and epoch settings. Results showed that the performance of both models was greatly improved with feature selection. Before feature selection, the accuracy and F1-Score of the AE-MLP are 99.2% and 99.3% whereas the accuracy and F1-Score of the MLP are 99.5% and approximately 68%. The results indicate that feature selection plays a significant role in reducing noise and enhancing the performance of DDoS detection in smart home environments.

Page 35 of 35 | Total Record : 343

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