cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nurul Khairina
Contact Email
nurulkhairina27@gmail.com
Phone
+6282167350925
Journal Mail Official
nurul@itscience.org
Editorial Address
Jl. Setia Luhur Lk V No 18 A Medan Helvetia Tel / fax : +62 822-5158-3783 / +62 822-5158-3783
Location
Kota medan,
Sumatera utara
INDONESIA
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 795 Documents
 35   36   37   38   39 
Enhanced Plant Disease Detection Using Computer Vision YOLOv11: Pre-Trained Neural Network Model Application Al Husaini, Muhammad; Rachmat Raharja , Agung; Cahaya Putra , Vito Hafizh; Lukmana, Hen Hen
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5146

Abstract

This study investigates the application of YOLOv11, a cutting-edge deep learning model, to enhance the detection of plant diseases. Leveraging a comprehensive dataset of 737 images depicting tomato leaves affected by various diseases, YOLOv11 was trained and evaluated on key performance metrics such as precision, recall, and mAP. Experimental results the model was trained and evaluated on key metrics including accuracy (75.6%), precision (0.80), recall (0.77), and mAP@0.5 (75.6%). Experimental through base architectural such as enhanced feature extraction with C2 modules, improved multi-scale detection using SPPF layers, and optimized non-maximum suppression techniques. These improvements enable the model to achieve stable precision and recall for each class, even in challenging scenarios with overlapping objects and diverse environmental conditions. By addressing practical usability challenges, this system offers a scalable, accessible, and impactful solution for precision agriculture, paving the way for sustainable with this pretrained model. This study underscores the potential of deep learning-based models, particularly YOLOv11, in transforming the way monitoring and disease management are approached, demonstrating its ability to stable accuracy and operational efficiency in real-world applications. Furthermore, the practical usability of the YOLOv11-based system addresses challenges in the domain of precision plant detection desease. By providing a scalable, accessible, and highly efficient solution, the model offering a significant advancement toward sustainable agricultural practices.
The Application of the FMADM Electre Algorithm in Diagnosing the Level of Drug Addiction in Adolescents Muchain, Alfira Nafhan; Zufria, Ilka; Fakhriza, M.
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5151

Abstract

Drug abuse among adolescents was difficult to identify early without official examinations, while manual methods were often inaccurate. The process of determining rehabilitation also faced challenges due to the lack of technology-based support systems capable of effectively analyzing the level of addiction and type of drug used, resulting in rehabilitation that was often not well-targeted. To address this issue, the algorithm was utilized to diagnose drug addiction in adolescents by providing scores or rankings indicating addiction levels: scores of 1 and 2 represented mild addiction, 3 and 4 indicated moderate addiction, and 5 or higher represented severe addiction. The FMADM-ELECTRE algorithm recommended various types of rehabilitation actions for recovery. It offered precise evaluation ranges and scores, simplifying the classification and determination of appropriate detoxification measures for each type of drug-addicted adolescent. This system classified three levels of drug addiction among adolescents, corresponding to three stages of rehabilitation for drug addicts: non-medical (social) rehabilitation, medical rehabilitation (detoxification), and aftercare (post-rehabilitation). Additionally, the web-based support system was designed to be accessible across various devices, including laptops, computers, tablets, and smartphones, facilitating quicker and more efficient decision-making for relevant institutions. This approach also integrated multi-criteria methods to ensure fairness and accuracy in analysis, supporting a comprehensive rehabilitation process.
Implementation of WSN and IoT to Monitor and Control Villa Electronic Equipment in Blankspot Areas Saifulloh, Muhammad; Santoso, Banu; Ariyus, Dony
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5165

Abstract

Maintaining a remote villa in a blank spot area presents challenges in ensuring optimal environmental conditions without the direct presence of the owner. This study aims to develop an Internet of Things (IoT)-based Wireless Sensor Network (WSN) system using the XBee S2C module with the Zigbee remote monitoring and control protocol. This system utilizes temperature, humidity, lighting, and water level sensors connected to electronic device controls such as lights, fans, and water pumps. Sensor Nodes are placed in the villa to collect data, while Coordinator Nodes are located in areas with internet access to upload data to the Thingspeak platform. Data is visualized through an interactive web interface that allows for remote control up to 1.03 km. The test results show a data transmission success rate of 100% with an average control response time of 6.5 and 9 seconds. This system offers the best solution for managing a villa in a blank spot area, making it easy for owners to monitor and control electronic equipment in real-time. This research contributes to developing WSN and IoT technologies, especially for applications in remote areas with website platform.
Development of Medical Record System Posyandu Taman Salak with Waterfall Method Budisaputro, Crismantoro; Kusdwiadji, Agustinus; Villasari, Asasih
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.5191

Abstract

One form of ICT utilization in the health sector is a digital-based health application. Health applications are included in the National Health System (SKN). Posyandu is a form of health service effort that is managed by, for and from the community with the aim of facilitating access to basic health services for mothers and children. Posyandu Taman Salak is one of the health facilities available in Madiun City. The process of recording and processing data on all Posyandu Taman Salak activities is still done by handwriting in a report book. This causes cadres to have difficulty monitoring children's growth and development and are overwhelmed in preparing reports to the integrated health post supervisors. Based on the description of the problems at the Taman Salak Posyandu, it is necessary to develop a Posyandu medical record system to make it easier for Posyandu cadres to process Posyandu data, monitor child growth and development and make reports. The purpose of this study is to design and build a Posyandu Taman Salak medical record system using the Waterfall system development modeling. The design stages use DFD and ERD. The system has been completed based on the website and has conducted functional system testing with the result that all system functions can be run
Comparison of Lexical and Semantic Approaches for Relevance Measurement in Quranic Verse Translation Retrieval Fauzan, Abd. Charis; Rouf, M. Abd.; Prabowo, Tito; Baqi, Utrodus Said Al
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5194

Abstract

This research explores the effectiveness of lexical and semantic approaches for relevance measurement in Quranic verse translation retrieval, focusing on Indonesian translations. Quranic verses encompass complex linguistic structures and diverse contexts, making precise retrieval challenging. Two retrieval methods were evaluated: lexical similarity, which focuses on exact word matches, and semantic similarity, which captures contextual meaning using word embeddings. The study utilized a dataset of Indonesian Quranic translations, preprocessed to normalize and tokenize text, with experimental queries derived from thematic exegesis on social responsibility. Evaluation was performed using precision, recall, and F1-score on top-5, top-10, and top-15 retrieved results. The lexical approach achieved perfect precision (100%) but exhibited lower recall (46%-58%), as it failed to retrieve relevant verses lacking exact matches. Conversely, the semantic approach demonstrated higher recall (56%-59%) and F1-scores (73%-74%) by identifying verses with contextual relevance, even in the absence of lexical similarity. The results reveal that while the lexical approach ensures precise matches, it overlooks semantic richness. The semantic approach, although computationally intensive, achieves greater contextual understanding. These findings highlight the potential for hybrid retrieval systems combining both approaches to enhance accuracy and relevance in Quranic information retrieval, supporting scholarly research and user engagement with Quranic content.
The Comparison of the K Mean Algorithm with the C 45 Algorithm in Dataming Applications: Balancing Precision and Speed in Data Mining Solutions Panggabean, Erwin; Simangunsong, Agustina; Sinaga, Dedi; Sihombing, Agus Putra Emas; Aritonang, Tri Evalina
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5319

Abstract

This research topic discusses the comparison of the K-Means and C4.5 algorithms in the application of data mining to predict aquarium sales in a company. K-Means is a clustering algorithm that functions to group data based on similarity, for example grouping customers based on frequency or type of purchase. This helps companies understand market segments and design marketing strategies accordingly. Meanwhile, C4.5 is a classification algorithm that builds decision trees based on important attributes that influence sales, such as price, season, or promotions. This algorithm is able to predict sales categories, such as increases or decreases, based on historical data. By comparing these two algorithms, the research sought to find out which algorithm is more effective in helping companies predict sales and make strategic decisions. A combination of the two can also be used, with K-Means grouping the data first, then C4.5 classifying each segment formed. These results can provide more accurate sales predictions and more effective marketing strategies. This research is important to understand the effectiveness of algorithms in data mining to improve business decision making.
The Design and Build a Web-Based Purchasing Information System using Agile Methods at Darma Store Ikhwan Muhsinin, Yahya; Sudarmilah, Endah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5368

Abstract

Increased electronic media can help business efficiency in customer service and sales. The implementation of Business to Customer (B2C) focuses on operational improvements which include developing more effective marketing strategies providing more responsive and personalized customer service which aims to improve customer experience and strengthen long-term business relationships. Software implementations are designed to improve operational processes, optimize workflows and minimize time required. This research aims to design and build a web-based online ordering and purchasing information system at the Darma Building Store. The method used in this research is software development. Agile Software Development uses agile methods which are very efficient and convincing in recognizing changes and higher customer satisfaction. The programming language used is Hypertext Preprocessor (PHP) with the Laravel framework. This research hopes that a web-based online ordering and purchasing information system can help the Darma Building Store manage inventory, increase operational efficiency, and making it easier for customers to make purchases. This system has several online ordering features, purchase reports and sales reports. This system is expected to help stores manage inventory, increase operational efficiency, and make it easier for customers to make purchases.
Optimization of the Shortest Route Using the Djikstra Algorithm to the Nearest Covid-19 Referral Hospital for Communities Exposed to the District of Medan Baru Siringoringo, Yan Batara Putra; Manurung, Asima; Br Tarigan, Enita Dewi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5374

Abstract

Abstract: Finding the shortest route is a problem to find a path that connects two nodes with the least amount of weight. Many methods are used in finding the shortest route. One of the methods used is Dijkstra's algorithm. Dijkstra's algorithm is an excellent algorithm used in determining the shortest route from a startingpoint toan end point (destination). In this study, the determination of the shortest route from each kelurahan in the Medan Baru District to the nearest Covid-19 referral hospital can be searched maximally using the Dijkstra algorithm with the distance taken through the google maps application. However, there are some limitations that are limitations in this study. The drawbacks are traffic jams, traffic lights, one-way streets. This cannot be ignored on routes in urban areas. In the future, researchers will look for optimization of determining the shortest route by including some of the problem constraints that occur. The Dijsktra algorithm is an application that must be modernized for more complex constraints.
Optimization of Electric Power Flow Analysis Using the Gauss-Seidel Method in a Numerical Approach E, Erwin; Arifin, Ilham; Panjaitan, Septhia Eka Nurviranthy; Manik, Graceya Zagita; Marsela, Wiwi; Manurung, Janter Ricardo
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5382

Abstract

The availability of electrical energy is a fundamental requirement in modern society, supporting both daily life and industrial activities. To ensure efficient and reliable energy distribution, power flow analysis is critical. This analysis is grounded in Kirchhoff's laws, which serve as the foundation for understanding electrical circuits. Kirchhoff's Current Law (KCL) states that "the sum of electric currents entering and leaving a branch point is zero," while Kirchhoff's Voltage Law (KVL) asserts that "the sum of electromotive forces and potential drops in a closed circuit must equal zero." These laws guide the formulation and solution of equations describing power flow in electrical networks. To manage the complexity of these systems, the Gauss-Seidel method has emerged as an effective iterative technique for solving large systems of linear equations. In the context of power flow analysis, it calculates busbar voltages, active and reactive power flows, and other parameters, refining the results through successive approximations until convergence is achieved. Python is widely recognized as an ideal platform for implementing the Gauss-Seidel method due to its syntactic simplicity, flexibility, and extensive computational libraries. By leveraging Python, engineers can streamline computations and enhance the accuracy and reliability of power flow analyses. This combination of mathematical rigor and computational power not only ensures precise results but also facilitates the efficient management of complex electrical systems in modern power grids.
A Novel Privacy-Preserving Algorithm for Secure Data Sharing in Federated Learning Frameworks Dalimarta, Fahmy Ferdian; Faoziyah, Nina; Setiawan, Doni
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5385

Abstract

Federated Learning (FL) has emerged as a promising paradigm for the collaborative training of machine learning models across decentralized devices while preserving data privacy. However, ensuring data security and privacy during model updates remains a critical challenge, particularly in scenarios that involve sensitive data. This study proposes a novel Privacy-Preserving Algorithm (PPA-FL) designed to enhance data security and mitigate privacy leakage risks in FL frameworks. The algorithm integrates advanced encryption techniques, such as homomorphic encryption, with differential privacy to secure model updates without compromising the utility. Furthermore, it incorporates a dynamic noise-adjustment mechanism to adaptively balance privacy and model accuracy. Extensive experiments on benchmark datasets demonstrate that PPA-FL achieves a competitive trade-off between privacy protection and model performance compared to existing methods. The proposed approach is computationally efficient and scalable, making it suitable for real-world applications in healthcare, finance, and the IoT environment. This research contributes to advancing secure data-sharing practices in federated learning, fostering the broader adoption of privacy-preserving machine learning solutions.

Page 37 of 80 | Total Record : 795

 35   36   37   38   39 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue Vol. 7 No. 2 (2025): Forthcoming: Research Article, Volume 7 Issue 2 April, 2025 Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025 Vol. 7 No. 4 (2025): Articles Research October 2025 Vol. 7 No. 3 (2025): Articles Research July 2025 Vol. 7 No. 1 (2025): Article Research January 2025 Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024 Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024 Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024 Vol. 6 No. 4 (2024): Articles Research October 2024 Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023 Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023 Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022 Vol. 4 No. 1 (2022): Article Research Volume 4 Number 1, Januay 2022 Vol. 3 No. 1 (2021): Computer Networks, Architecture and High Performance Computing, January 2021 Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July Vol. 2 No. 2 (2020): Computer Networks, Architecture and High Performance Computing Vol. 2 No. 1 (2020): Computer Networks, Architecture and High Performance Computing Vol. 1 No. 2 (2019): Computer Networks, Architecture and High Performance Computing Vol. 1 No. 1 (2019): Computer Networks, Architecture and High Performance Computing More Issue