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All Journal Secure And Knowledge-Intelligent Research in Cybersecurity And Multimedia (SAKIRA) IJMST
Muhammad Jordan
Universitas Islam Al-Azhar
Computer Science & IT Engineering Law, Crime, Criminology & Criminal Justice

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Analisis QoS (Quality Of Service) Pada Jaringan Rt/Rw Net Untuk Peningkatan Efisiensi Dan Produktivitas Layanan Muhammad Jordan; Khairul Warisin Hammami; Firmansyah Firmansyah
Secure And Knowledge-Intelligent Research in Cybersecurity And Multimedia (SAKIRA) Vol. 2 No. 1 (2024): Multimedia Integration and computer networks in the Development of Modern Infor
Publisher : Universitas Islam Al-Azhar Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36679/s4kira.v2i1.32

Abstract

In an increasingly advanced digital era, computer networks play an important role in supporting various activities, including in the community-initiated RT/RW Net environment. This research aims to analyze Quality of Service (QoS) on the RT/RW Net network to improve service efficiency and productivity. The research method used is the experimental method, which involves data collection through the Mikhmon application to monitor network performance. QoS measurements were made based on four main parameters: throughput, delay, jitter, and packet loss.The results show that the RT/RW Net network has excellent throughput with a value of 1471 Kb/s, average delay of 3.623 ms, jitter of 3.63 ms, and packet loss of 0%, all of which fall into the "Very Good" category according to Tiphon standards. Security testing was also conducted by simulating a brute force attack, which revealed several weaknesses in the security system that require improvement.The conclusion of this study is that the RT/RW Net network has a very good quality of service, but needs improvement in the security aspect. Suggestions include increasing bandwidth capacity, implementing stricter security policies, and improving connection stability.
Comparative Analysis of YOLOv8 and YOLOv11 Algorithms for Real-Time Vehicle Detection Muhammad Jordan; Firmansyah; Hasan Koten
Indonesian Journal of Modern Science and Technology Vol. 2 No. 2 (2026): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/

Abstract

Intelligent transportation systems required rapid and highly accurate object detection algorithms to function effectively in real-time environments. Although the You Only Look Once (YOLO) architecture evolved significantly to address these demands, comprehensive performance comparisons between its latest iterations remained sparse in the context of urban traffic. Selecting the optimal model for specific deployment constraints remained a critical challenge for developers. To address this gap, this research comparatively evaluated the performance of YOLOv8 and the newly released YOLOv11 specifically for vehicle detection tasks. The primary contribution was providing a rigorous, empirical head-to-head comparison to guide model deployment based on specific accuracy needs and hardware constraints. The methodology employed a standardized public dataset of traffic imagery containing various vehicle classes. Both nano variants, namely YOLOv8n and YOLOv11n, were trained from scratch under identical hardware environments and hyperparameter settings. Specifically, the training utilized the AdamW optimizer for fifteen epochs with a batch size of sixteen. Performance was quantitatively measured using Precision, Recall, mean Average Precision (mAP50 and mAP50-95), and computational resource allocation in terms of parameter size. The experimental results indicated a nuanced performance dynamic. YOLOv11n demonstrated a vastly superior Precision score of 0.784 compared to 0.672 for YOLOv8n, signifying its enhanced reliability with fewer false alarms due to refined spatial attention modules. Conversely, YOLOv8n outperformed YOLOv11n in Recall (0.695 versus 0.662) and mAP50 (0.746 versus 0.740). This suggested that YOLOv8n achieved faster learning convergence under constrained training conditions. Both models maintained real-time processing capabilities suitable for edge devices. In conclusion, YOLOv11n was recommended for high-accuracy traffic surveillance systems prioritizing precision, whereas YOLOv8n remained a highly viable architecture for hardware-constrained applications that prioritized high recall and aggressive object detection.
Co-Authors Firmansyah Firmansyah Firmansyah Hasan Koten Khairul Warisin Hammami