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All Journal Scientific Journal of Engineering Research Scientific Journal of Computer Science
Abdel-Nasser Sharkawy
Mechanical Engineering Department, Faculty of Engineering, South Valley University
Computer Science & IT Engineering

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Bandwidth Management Using the Hierarchical Token Bucket Method to Enhance Server Network Performance Ahmad Jayadi; Dedi Satriawan Kusnayadi; Syahrani Lonang; Abdennasser Dahmani; Zied Driss; Abdel-Nasser Sharkawy
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.40

Abstract

Villa Nomada, as an accommodation in Kuta, Central Lombok, is experiencing internet network instability due to uneven and uncontrolled bandwidth distribution, which disrupts user comfort, especially for foreign guests who require an optimal connection. The solution implemented is bandwidth management using the Hierarchical Token Bucket (HTB) method to allocate bandwidth fairly and efficiently. This research contributes to improving quality of service (QoS) by optimizing network performance through HTB. The method used is HTB configuration to allocate bandwidth based on user categories (VIP, Regular, and Office). Network performance was evaluated before and after implementation to measure improvements in speed and stability. The research results showed that HTB successfully distributed bandwidth evenly, with VIP users receiving priority, while regular and office users obtained stable connections without interruptions. Network efficiency improved, reducing congestion and increasing user satisfaction. We rated the HTB method as “Good” for optimizing network performance. In conclusion, the implementation of HTB successfully addressed the bandwidth management issues at Villa Nomada, ensuring fair distribution and optimal network performance for all users.
Trends and Impact of the Viola-Jones Algorithm: A Bibliometric Analysis of Face Detection Research (2001-2024) Setiawan Ardi Wijaya; Tri Stiyo Famuji; Muhammad Amirul Mu'min; Yana Safitri; Novi Tristanti; Abdennasser Dahmani; Zied Driss; Abdel-Nasser Sharkawy; Raheem Al-Sabur
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): March
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.8

Abstract

The Viola-Jones algorithm remains a cornerstone in computer vision, particularly for object and face detection. This bibliometric study provides a comprehensive analysis of the algorithm’s academic impact and research trends, encompassing publication patterns, citation metrics, influential authors, and co-occurrence of keywords. The findings indicate a significant rise in research outputs and citations between 2016 and 2020, reflecting the algorithm's sustained relevance and application in various domains. Network visualization maps further reveal the algorithm's integration with diverse fields, including machine learning, image processing, and neural networks, emphasizing its versatility and adaptability to emerging technological challenges. Key research contributions include advancements in hybrid approaches, combining the Viola-Jones framework with techniques such as convolutional neural networks and HOG-SVM for improved detection accuracy. However, limitations such as computational inefficiency and sensitivity to environmental factors persist, presenting opportunities for innovation. This study concludes by highlighting future research directions, such as integrating deep learning and edge computing to enhance algorithmic performance in real-time and complex scenarios. This study provides a valuable reference for researchers and practitioners aiming to extend the Viola-Jones algorithm’s capabilities and applications by consolidating existing knowledge and identifying research gaps.
Classification for Waste Image in Convolutional Neural Network Using Morph-HSV Color Model Miftahuddin Fahmi; Anton Yudhana; Sunardi; Abdel-Nasser Sharkawy; Furizal
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): March
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.12

Abstract

Waste management is essential in preserving nature to be cleaner and more well-maintained. Waste management runs slower than the speed of waste accumulation. One reason is slow waste sorting. This problem can be overcome by building a learning machine that can sort the types of waste. The type of waste often separated in the first sorting is waste based on its type, namely organic and inorganic. The classification model used is the CNN with image processing Morph-HSV color model. The data obtained from Kaggle is collected and processed using Python. The processed image is trained using a CNN classification model. The results of this study are an accuracy of 99.58% and a loss of 1.57%. With this research, it is hoped that it can accelerate waste sorting performance using the most efficient ML based on image processing and its classification model.
Co-Authors Abdennasser Dahmani Ahmad Jayadi Anton Yudhana Dedi Satriawan Kusnayadi Furizal Miftahuddin Fahmi Muhammad Amirul Mu'min Novi Tristanti Raheem Al-Sabur Setiawan Ardi Wijaya Sunardi Syahrani Lonang Tri Stiyo Famuji Yana Safitri Zied Driss