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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 795 Documents
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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.
Diving Into Public Sentiments: SVM-Based Analysis of Water Service Opinions Hasugian, Aldi Ridwansyah; Zufria, Ilka
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.v7i1.5231

Abstract

Customer satisfaction is a crucial measure of a company's success, particularly for service-oriented businesses such as regional water utilities. In the case of PDAM Tirta Nauli Sibolga, key service aspects including pricing, water quality, water flow, and responsiveness to customer complaints significantly influence customer satisfaction. Despite ongoing efforts to provide quality services, challenges in areas such as water quality, quantity, and customer communication persist, leading to lower satisfaction levels among consumers. This study seeks to evaluate the implementation of regional regulations related to company management and clean water services, with a focus on improving service quality. Additionally, it applies the Support Vector Machine (SVM) algorithm to perform sentiment analysis of public opinions regarding the company's services. The analysis yielded an impressive accuracy of 98%, with precision, recall, and F1-score values consistently above 90%, indicating a high level of effectiveness in sentiment classification. By leveraging the results of this sentiment analysis, PDAM Tirta Nauli can gain valuable insights into the issues facing the community and implement targeted improvements. Ultimately, this research aims to provide a comprehensive evaluation of customer satisfaction and offer actionable recommendations to enhance service quality, ensuring better customer experiences and supporting the overall development of the region.
Analysis of Outpatient Patient Visit Prediction at Muntilan Regional General Hospital Using Linear Regression Method Susanti, Dwi; Hendradi, Purwono; Abul Hasani, Rofi
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.v7i1.5271

Abstract

Hospitals play a crucial role in public health, and understanding patient visit patterns is essential for effective service delivery. Thus, accurate predictions are vital for resource planning, service improvement, and addressing challenges like long wait times and overcrowding. This study focuses on predicting outpatient visits at RSUD Muntilan, a regional general hospital in Magelang, Indonesia. The method used was the linear regression method. The research involved data collection from the hospital's information system, pre-processing to prepare the data, dataset formation, model creation using linear regression, and model evaluation. The study utilized historical outpatient visit data FROM 2021 TO 2024 to develop a linear regression model that predicts the number of visits for the next three months. The model's performance was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 15.33%. This indicates that the model's predictions were, on average, within 15.33% of the actual values, demonstrating an accuracy of 84.67%. The successful application of the linear regression method in this study highlights its potential for improving resource allocation, enhancing service efficiency, and ultimately enhancing the overall quality of healthcare services provided by RSUD Muntilan. The findings emphasize the significance of data-driven approaches and predictive analytics in optimizing healthcare operations and meeting the evolving needs of the community. 
Web-Based E-Commerce Using Up Selling and Safety Stock Methods Amanda, Widia Putri; Samsudin
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.v7i1.5279

Abstract

This research examines the application of the e-commerce platform at the An-Nur Muslim Shop which sells Hajj and Umrah equipment. In the rapidly developing digital era, e-commerce has become essential for companies to expand market share and increase operational efficiency. E-commerce is changing the traditional way of commerce, allowing customers to shop easily and safely. The principle of fair and legal transactions in Islam is an important basis for the development of e-commerce, as emphasized in QS An-Nisa: 29. The Up Selling method is applied to increase sales by offering similar products that have a higher value, which is expected to encourage customers to buy more products. Up Selling works by sorting products from lowest to highest price and offering discounts and special promos on products that meet the criteria. In addition, the Safety Stock concept is used to manage inventory more efficiently. Safety Stock helps anticipate demand uncertainty and delays in receiving raw materials, maintaining optimal stock levels. With this approach, An-Nur Moeslim Shop’s can keep stock levels low to reduce inventory management costs and match new orders only when stock is low. The proposed e-commerce system will allow users to search for products, review descriptions and prices, and make purchases easily through a user-friendly web interface. It is hoped that this e-commerce platform will strengthen An-Nur Moeslim Shop’s position in the market, provide significant added value, and improve the overall customer shopping experience.
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.
CRYPTOGRAPHY OF CHACHA20 and RSA ALGORITHMS for TEXT SECURITY Dzahabi, Ziad Yasqi; Hayaty, Nurul; Bettiza, Martaleli
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.v7i1.5345

Abstract

The purpose of this study is to apply the ChaCha20 and RSA cryptographic algorithms to enhance text security and safeguard data from unauthorized access, data breaches, and cyberattacks such as man-in-the-middle or replay attacks. ChaCha20, a symmetric encryption algorithm, is employed for generating efficient and secure keystreams, while RSA, an asymmetric algorithm, is used for encrypting numeric keys or messages. The integration of these two algorithms ensures robust data protection from various digital threats. The choice of this title stems from the growing urgency to prioritize data security in the digital era, especially given the increasing incidents of data leaks that often lead to significant consequences. This research focuses on analyzing the implementation of both algorithms in encryption and decryption processes, as well as evaluating their effectiveness in preserving data confidentiality and integrity. The findings of this study demonstrate that the ChaCha20 and RSA implementations effectively secure data, with the encryption and decryption processes functioning as intended. To further validate the system’s robustness, simulated attacks were conducted, and the results confirmed the system's ability to prevent unauthorized access. This research not only contributes to the development of reliable data security solutions but also highlights opportunities for future improvements. Enhancing algorithm efficiency and optimizing encryption runtime are potential areas for further exploration. By addressing these challenges, the study aims to pave the way for more robust and efficient cryptographic solutions in the evolving landscape of digital security.

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