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Nurul Khairina
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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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Patient Management System Using Fuzzy Multiple Attribute Decision Making Method with SAW at Noura Aesthetic Clinic Zahary, Fahmi; Kurniawan, Rahmat
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4366

Abstract

This study presents the development and implementation of a Patient Management System (PMS) at Noura Aesthetic Clinic using the Fuzzy Multiple Attribute Decision Making (FMADM) method with Simple Additive Weighting (SAW). The aim is to enhance the decision-making process for patient treatment prioritization and management. The PMS integrates various patient attributes, including medical history, treatment urgency, and resource availability, into a comprehensive decision-making framework. By employing the FMADM method, the system addresses the inherent uncertainties and subjectivities in patient data, ensuring more accurate and reliable prioritization. The SAW technique further refines this process by assigning weighted scores to each attribute, facilitating a straightforward and effective comparison. This combination allows for a balanced assessment of multiple factors, promoting optimal resource allocation and improving overall patient care. The implementation at Noura Aesthetic Clinic demonstrated significant improvements in operational efficiency and patient satisfaction. The system's adaptability to diverse clinical settings and its user-friendly interface make it a valuable tool for healthcare providers. This study underscores the potential of advanced decision-making methodologies in transforming patient management practices, paving the way for more informed and equitable healthcare delivery.
Literature Review Application of YOLO Algorithm for Detection and Tracking Feri Imanuel; Waruwu, Seven Kriston; Linardy, Alvin; Husein, Amir Mahmud
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4374

Abstract

A vehicle tracking system is a computer program that utilizes devices to monitor the position, movement and condition of a vehicle or fleet of vehicles. Multi-vehicle tracking on highways has significant research interest and practical value in building intelligent transportation systems. Nevertheless, traffic road video frames consist of various complex backgrounds and objects. Detection and tracking are very challenging because foreground to background switching occurs frequently. One-stage algorithm approaches such as YOLO and its various variants have been proven to be accurate for detecting vehicles. Meanwhile, the SORT, DeepSORT, ByteTrack and other algorithms can be combined in YOLO. The aim of this study is to highlight existing research on the application of YOLO and its variants in detecting and tracking vehicles, especially in traffic management. The journals used are limited to 2019 – 2024 and the journal sources consist of Hindawi, IEEE, MDPI, Research Gate, Science Direct, and Springer. Based on the research that has been reviewed, the YOLO variant algorithm approach has been successfully applied in the field of vehicle monitoring to support smart cities. In addition, many new model combinations and improvements have been proposed, proving that this algorithm has a big influence in the field of computer vision.
YOLO-Based Vehicle Detection: Literature Review: English Kosasi, Tommy; Sihombing, Zein Adian Laban; Husein, Amir Mahmud
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4377

Abstract

This research aims to evaluate the implementation of the You Only Look Once (YOLO) algorithm and its variants in the context of vehicle detection in traffic management systems. The importance of implementing intelligent transportation systems (ITS) in increasing transportation efficiency and reducing traffic problems such as congestion and accidents. The methodology used involves a critical review of current literature utilizing the YOLO algorithm for vehicle detection, with a focus on improving the accuracy of detection models. The research results show that the YOLO algorithm and its variants, such as YOLOv4 and YOLOv8, show a significant increase in vehicle detection accuracy reaching 90% in various environmental conditions. However, weaknesses in detecting small objects and in extreme lighting conditions still need further attention. This study also reviews several improvement approaches proposed in the literature, including the use of image augmentation techniques and the integration of deep learning models to improve the performance of the YOLO algorithm. The implementation of the YOLO algorithm in vehicle detection in intelligent transportation systems has great potential in increasing the efficiency and accuracy of traffic monitoring. This research provides recommendations for further development so that the YOLO algorithm can be better adapted to various environmental conditions and different types of data.
Evaluation of Information Technology Service Devices At High Schools in Kendal Regency With ITIL 4.0 Herlinudinkhaji, Didin; Purnamasari, Dewi; Erwanti, Nindita
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4382

Abstract

Schools as educational institutions with information technology services in the form of New Student Admission Systems, e-report systems, and other information technology services require devices supported by good service. Information technology devices must be appropriately managed to conduct business processes according to business goals. With good service management, the business value offered to customers (students) will increase. ITIL V4 contains information technology service management guidelines that contain best practices for information technology service management, including attention to customer experience, value streams, and support for current digital transformation. This research measures information technology equipment services at high schools in Kendal Regency. This research uses indicators adapted to schools with ITIL V4: Key Performance Indicators (KPI) and Critical Success Factors (CSF). The results of this research show that the performance of the hardware needs to be improved, and the value of the maturity model for each service device is still not good.
Performance Analysis of an E-commerce Website Using Distributed Servers (Case Study: Ecommerce Bumdes Sarining Kukuh Winangun) Satwika, I Kadek Susila; Andika, I Gede
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4383

Abstract

Digital transformation has significantly altered our interactions by leveraging the internet for fast global information exchange and collaboration. This shift impacts businesses, individuals, organizations, and governments by increasing the demand for fast, reliable web services. To meet these demands, many organizations are turning to distributed server infrastructures, which help handle large storage needs and avoid performance issues. This research focuses on creating a distributed web server system that uses Round-Robin load balancing to evenly distribute traffic and enhance performance. The Round-Robin algorithm’s simplicity and effectiveness in balancing loads, combined with the advantages of virtualization, such as cost efficiency, improved performance, and better resource management are central to this approach. Virtualization also offers improved scalability, accuracy, and security, further enhancing overall system efficiency and effectiveness in data centers. This research evaluates and contrasts the performance of websites utilizing distributed servers against those using single servers. The findings indicate that websites with distributed servers significantly outperform those with single servers. Specifically, distributed servers offer response times that are 5.8 times faster, achieve 2.2 times more successful responses, and transfer 2.1 times more data than single servers. Additionally, single servers experience a much higher rate of timeouts, with 14.2 times more occurrences compared to distributed servers.
Evaluation of Determining the Best Product Promotion Media Decisions for MSMEs with ROC Ranking Technique and SAW Ranking Method Soleman, Soleman
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4386

Abstract

Micro, Small, and Medium Enterprises (MSMEs) face challenges in determining effective promotional media to increase sales of their products. This research aims to evaluate and determine the best promotional media for MSMEs using the ROC (Rank Order Centroid) ranking technique and the SAW (Simple Additive Weighting) method. The criteria used include Advertising Costs, Target Market, Promotion Time, and Brand Image. Based on the calculation of criteria weights with the ROC technique and evaluation of alternatives with the SAW method, Social Media was found to be the best promotional media, followed by Search Engines and Product Collaboration. Social Media has advantages in low cost, wide target market reach, and promotional flexibility and effectiveness. Search Engines and Product Collaboration also showed good results in reaching the target market and promotional flexibility. In contrast, Print Media ranked the lowest due to limited reach and high costs. This research provides guidance for MSMEs in choosing the right promotional media and optimizing resources for maximum promotional results.
Implementing Preference Selection Index for Optimal Employee Ranking in Organizational Decision-Making Wijanarko, Rony; Nugroho, Fifto; Islam, Khoirul
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4387

Abstract

The rapid development of information technology has affected various aspects of life, including in the world of work. This research aims to apply the Preference Selection Index (PSI) method in determining the best employees at Bina Karya Utama Company. The assessment is based on four main criteria: Attendance, Tardiness, Overtime, and Length of Service. Data is obtained through observation and interviews, then processed using the PSI method which involves the normalization process and the calculation of preference values. The results showed that employees with alternative code A8 obtained the highest score, followed by A5 and A9. The PSI method proved to be effective in helping companies make objective and fair decisions, as well as motivating employees to improve their performance. This research concludes that a PSI-based decision support system can improve transparency and fairness in employee evaluation at Bina Karya Utama Company.
Optimizing Decision-Making for Aid Allocation in Underdeveloped Regions Using the MOORA Method Wijaya, Vera; Nugroho, Fifto; Kraugusteeliana, Kraugusteeliana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4389

Abstract

The allocation of assistance for the Family Hope Program is a process that requires precision to ensure that assistance is given to those most in need. This research develops a Decision Support System (DSS)  using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method for optimizing the selection of beneficiaries in disadvantaged villages which includes criteria used including education, toddlers, pregnant women, disabilities, elderly, income, employment, number of dependents, and house size. Each criterion is normalized and given a weight according to its level of importance. The results show that alternative A2 has the highest optimization value with Yi of 0.254, followed by A8 (0.208) and A5 (0.204). In contrast, alternatives A3 (0.029) and A10 (0.035) have the lowest optimization value. Matrix normalization and criteria weights show the significant influence of the criteria of education, pregnant women, elderly, income, number of dependents, and house size in the selection process. The implementation of DSS with the MOORA method is proven to increase efficiency and accuracy in the selection process of Family Hope Program beneficiaries, reduce subjective errors, and ensure assistance is channeled to those who really need it. Therefore, the MOORA method is recommended as an effective tool to optimize social assistance allocation, increase transparency, and reduce bias in decision-making.
Sentiment Analysis of Oppenheimer Movie Reviews: Naïve Bayes Algorithm for Public Opinion Noviansyah, Berliana; Effendi, Muhammad Makmun; Achmad, Yudianto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4393

Abstract

The development of information and communication technology has revolutionized the way people consume and engage with media, particularly in the realm of film. Online platforms such as Netflix, Amazon Prime Video, and YouTube have transformed movie consumption habits, providing a vast array of options for viewers to explore and enjoy. A crucial aspect of this digital landscape is the proliferation of movie reviews, which serve as valuable guides for users seeking to discover films aligned with their preferences. However, the abundance of reviews, often varying in quality and objectivity, necessitates tools capable of effectively processing and understanding these textual data. This research delves into sentiment classification of Oppenheimer movie reviews, utilizing the Naive Bayes algorithm to categorize reviews into positive, negative, and neutral sentiments. The dataset comprising audience reviews and numerical ratings undergoes preprocessing using the TF-IDF method to facilitate numerical representation. Subsequently, the Naïve Bayes algorithm is trained on this processed data to accurately classify sentiments. The model demonstrates exceptional performance, achieving an accuracy rate of 97.45% in distinguishing between positive, negative, and neutral sentiments within Oppenheimer movie reviews. This study underscores the efficacy of the Naive Bayes algorithm in sentiment classification and emphasizes the significance of employing techniques like TF-IDF for enhancing sentiment analysis in the domain of movie reviews.
Comparative Analysis of Naïve Bayes and K-Nearest Neighbor (KNN) Algorithms in Stroke Classification Iswara, Ida Bagus Ary Indra; Anandita, Ida Bagus Gede; Dahul, Maria
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4395

Abstract

Stroke, also known as cerebrovascular, is a type of Non-Communicable Disease (NCD). The symptoms of this disease arise due to a blockage (ischemic) or rupture (hemorrhagic) of a blood vessel that disrupts blood flow to the brain. This condition causes a lack of oxygen and nutrients to brain cells, resulting in damage and potentially death. This research aims to compare the use of Naive Bayes and K-Nearest Neighbor (K-NN) algorithms in classifying stroke diseases. The research process involves data collection, data validation, data preprocessing, data reading, data transformation, data splitting, model implementation, classification evaluation, application of Naive Bayes and K-Nearest Neighbor (K-NN) algorithms, and comparative analysis of results. The variables used in this study include: gender, age, hypertension, heart disease, ever married, work type, residence type, avg glucose level, bmi, smoking status, stroke. Sugar, BMI, Smoking Status, Stroke. Based on the experiments conducted, it was found that the Naive Bayes algorithm achieved an average accuracy rate of 91.67%, while the K-Nearest Neighbor (K-NN) algorithm achieved an average accuracy rate of 95.59%. Therefore, it can be concluded that the K-Nearest Neighbor (K-NN) algorithm has a higher average accuracy rate than the Naive Bayes algorithm, with a percentage difference in accuracy of 3.92%.

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