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Journal of Computer Networks, Architecture and High Performance Computing
Published by Yayasan Cita Cendekiawan Al Khwarizmi
ISSN : 26559102     EISSN : 26559102     DOI : 10.47709
Core Subject : Science, Education,
Computer Science & IT Education
Journal of Computer Networks, Architecture and Performance Computing is a scientific journal that contains all the results of research by lecturers, researchers, especially in the fields of computer networks, computer architecture, computing. this journal is published by Information Technology and Science (ITScience) Research Institute, which is a joint research and lecturer organization and issued 2 (two) times a year in January and July. E-ISSN LIPI : 2655-9102 Aims and Scopes: Indonesia Cyber Defense Framework Next-Generation Networking Wireless Sensor Network Odor Source Localization, Swarm Robot Traffic Signal Control System Autonomous Telecommunication Networks Smart Cardio Device Smart Ultrasonography for Telehealth Monitoring System Swarm Quadcopter based on Semantic Ontology for Forest Surveillance Smart Home System based on Context Awareness Grid/High-Performance Computing to Support drug design processes involving Indonesian medical plants Cloud Computing for Distance Learning Internet of Thing (IoT) Cluster, Grid, peer-to-peer, GPU, multi/many-core, and cloud computing Quantum computing technologies and applications Large-scale workflow and virtualization technologies Blockchain Cybersecurity and cryptography Machine learning, deep learning, and artificial intelligence Autonomic computing; data management/distributed data systems Energy-efficient computing infrastructure Big data infrastructure, storage and computation management Advanced next-generation networking technologies Parallel and distributed computing, language, and algorithms Programming environments and tools, scheduling and load balancing Operation system support, I/O, memory issues Problem-solving, performance modeling/evaluation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 813 Documents
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Generalized Chatterjea Type Contractions on Integrated Matrix Graph Metric Spaces Vinsensia, Desi; Utami, Yulia; G.S, M. Kurniawan; Virna, Lira
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7882

Abstract

This paper proposes a computationally verifiable integrate fixed point framework on the integrated metric space , where combines a continuous component endowed with the matrix induced metric with invertible and a discrete component defined by the shortest-path metric of a finite weighted graph. The objective is to obtain verifiable conditions that guarantee existence, uniqueness, and predictable convergence of fixed points for coupled continuous–discrete dynamics, while embedding the graph geometry directly into the metric via the scaling parameter . Our method studies the coupled operator and derives explicit sufficient inequalities ensuring that satisfies a Chatterjea-type contraction on , yielding an effective contraction factor . In particular, the threshold implies that admits a unique fixed point and that the hybrid Picard iteration converges geometrically in . Numerical experiments support these findings and clarify the integrate mechanism, when maps every vertex to a fixed node, the discrete mode stabilizes after the first iterate, and the successive iterate error decays exponentially at a rate consistent with , with numerical and analytic fixed points agreeing up to floating-point tolerance. Practically, the bound provides an a priori, computable convergence for implementations of matrix graph iterations relevant to graph structured computing and networked models. Future work includes reducing conservatism in the sufficient bounds, exploring richer couplings, and extending the analysis to broader graph classes.
Evaluation of Data Exposure Risks on Unencrypted Application Layer Protocols in RT/RW Net "X" Community Network Using NIST SP 800-86 Framework Reza Febriana; Muhammad Sidik Asyaky
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 2 (2026): Research Paper April 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i2.8014

Abstract

Security vulnerabilities in community-based networks, such as RT/RW Net, remain a critical concern due to the widespread use of unencrypted protocols. This study presents a quantitative evaluation of data exposure risks in application-layer protocols, focusing on HTTP traffic in local community networks. Using a network forensics approach based on the NIST SP 800-86 framework, traffic was captured and analyzed to measure the frequency and magnitude of sensitive data leaks using automated tools for network traffic analysis. The study quantified exposure across four key indicators: user credentials, session tokens, cookies, and personal information. The results indicated a high level of exposure, with analyzed HTTP packets successfully revealing sensitive data in plaintext, including usernames and passwords. Furthermore, statistical analysis of communication patterns identified significant opportunities for eavesdropping and session hijacking due to the lack of encryption standards. This evaluation provides empirical evidence of critical security gaps in RT/RW Net infrastructure and emphasizes the urgent need to transition to encrypted protocols (HTTPS). The findings provide a quantifiable risk assessment that can serve as a basis for implementing mitigation strategies in community-scale network management.
Deep Learning–Based Forest Fire Classification Using MobileNetV3, ResNet50, and YOLOv8 Djarot Hindarto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 2 (2026): Research Paper April 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i2.8112

Abstract

Forest and land fires pose significant environmental, economic, and public health challenges worldwide, particularly in regions with extensive forest coverage and prolonged dry seasons. Early and accurate detection is essential to mitigate damage and support rapid response efforts. This study proposes a deep learning–based approach for forest fire image classification using three prominent models: MobileNetV3, ResNet50, and YOLOv8. A curated dataset of forest fire images was employed, consisting of fire and non-fire scenes captured under diverse environmental conditions, including variations in illumination, smoke density, and background complexity. Prior to model training, all images underwent preprocessing steps such as resizing, normalization, and data augmentation to improve robustness and generalization. The performance of each model was evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa. Experimental results indicate that YOLOv8 achieved the best overall performance, with an accuracy of 0.952, precision of 0.9566, recall of 0.952, F1-score of 0.9519, MCC of 0.9412, and Kappa of 0.9400. ResNet50 demonstrated competitive performance with an accuracy of 0.940, slightly outperforming MobileNetV3, which achieved an accuracy of 0.938. The findings highlight that while lightweight architectures such as MobileNetV3 provide efficient performance suitable for resource-constrained environments, more advanced detection frameworks like YOLOv8 offer superior classification capability. Overall, this research demonstrates the effectiveness of modern deep learning models for automated forest fire image classification and supports their potential deployment in real-time early warning and environmental monitoring systems.

Page 82 of 82 | Total Record : 813

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