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
 76   77   78   79   80 
Twitter Sentiment Towards 2024 Jakarta Governor Candidates With Naïve Bayes Algorithm Abei, Fikri; Sulaeman, Asep Arwan; Suprapto, Suprapto
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.v7i1.5358

Abstract

This study aims to analyze public sentiment towards candidates for the 2024 Governor of DKI Jakarta through the Twitter platform, with a focus on classifying positive and negative sentiment. Along with the rapid development of social media, Twitter has become the main channel for people to voice political opinions. Sentiment analysis was conducted using the Naive Bayes algorithm to classify the sentiment of tweets collected through crawling techniques during the campaign period. The data used includes user tweets, with features such as frequently occurring words, popular hashtags, and discussion topics related to each gubernatorial candidate. The results showed that the Naive Bayes algorithm provided the best performance in classifying sentiment data in the period August 1 to December 26, 2024, with the highest accuracy rate reaching 75% at a data ratio of 90:10. This research also identified challenges in sentiment classification, such as the presence of new terms in test documents that are not recognized by the training model. The findings are expected to provide a clearer picture of public perceptions of gubernatorial candidates and contribute to the analysis of political sentiment on social media
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.
Analysis of Detergent Inventory Stock at Luch Laundry Using the Linear Regression Method Sinaga, Bosker; Tarigan, Nera Mayana Br; Marpaung, Rahmadina; Zamili, Kristof Rian
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.v7i1.5396

Abstract

Inventory stock management is an important aspect in the laundry business to ensure smooth operations and minimize costs. Laundry Detergent shortages or overstocks can cause service disruptions and unnecessary additional costs. Therefore, a method is needed that can help predict stock needs accurately, one of which is the linear regression method. The data used includes historical data on detergent use and other factors that influence demand over several time periods. Through linear regression analysis, a predictive model can be built to estimate detergent needs in the future, so that stocks can be managed more efficiently. Research Method, namely the survey research method, is a research method carried out using surveys or direct data collection from Laundry Luch. The method/algorithm used to analyze the data is the linear regression method. The aim of this research is to apply the linear regression method in detergent inventory stock and to carry out analysis using the linear regression method in detergent inventory stock. The research results from the data that have been collected show that the predicted stock of detergent supplies for Laundry Luch in January 2025, with an estimated total usage of 111 boxes of detergent and a target usage of 95 boxes of detergent, is 129 boxes of detergent. The research conclusion is that the linear regression method provides real benefits in supporting data-based decision making.
Classification of Scholarships for Students in Schools Using the Naïve Bayes Method Rizki Siregar, Awal; Furqan, Mhd.
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.v7i1.5417

Abstract

This research addresses the challenge faced by educational institutions in selecting scholarship recipients by implementing the Naïve Bayes algorithm. The objective of this study is to simplify and improve the accuracy of the scholarship selection process at MTs As-Syarif Kuala Beringin, using data from 50 students. The background highlights the importance of scholarships in providing equal educational opportunities, particularly for students with financial challenges. The research method involves the use of Naïve Bayes to calculate the probability of eligibility based on academic performance, economic background, and student activity. The results show that seven students met the scholarship criteria, demonstrating the efficiency and objectivity of the algorithm. The practical implications include the development of a user-friendly application that facilitates data input, scholarship criteria determination, and clear evaluation results. This system enhances transparency and reliability in decision-making. In conclusion, the Naïve Bayes algorithm proves to be an effective and efficient tool for scholarship selection, enabling a more equitable opportunity for students. Further research could focus on integrating additional data points or comparing the algorithm's performance with other classification methods to enhance system reliability.
The Website-Based Information System Design At Cahaya Harapan Jaya Building Shop Sutrisno, Iwan; 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.v7i1.5451

Abstract

Building material stores play an important role in providing building materials, but many still rely on conventional management systems that are less efficient in recording stock, transactions, and employee data. This results in delays in recording transactions, errors in stock monitoring, and the lack of an effective system in managing employee data. Therefore, this study aims to design a web-based information system to improve the operational efficiency of building material stores. This study uses the Waterfall method which consists of needs analysis, design, programming, testing and implementation of this system also uses blackbox testing and system usability scale (SUS) testing. This system is designed to make it easier for administrators and employees to manage products, transactions, employee data, and financial reports in real-time. The results of the study show that the web-based information system improves the efficiency of transaction recording, minimizes stock monitoring errors, and simplifies the management of employee data and financial reports. This system allows building material stores to optimize operational performance, improve data accuracy, and provide better customer service.
Profile Matching Method In Improving Staff Performance In Asahan University Irwansyah, Bambang; Harmayani, Harmayani; Apdilah, Dicky
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.v7i1.5463

Abstract

Asahan University (UNA) is one of the private universities in North Sumatra which has Institution B accreditation in 2024 which is led by the Chancellor Prof. Dr. Tri Harsono, M.Si, in improving quality in the field of administration, UNA always requires staff to give good performance according to their expertise. Objective assessment of job placement will improve performance and motivate staff to perform well. However, the process of placing staff positions at UNA is only based on tenure so that the performance process is less than optimal. Objective: Implementing a system that can provide an assessment of positions in accordance with their expertise. 50 employee data, diploma data, training certificates, work performance, work experience, work period and discipline. Based on the analysis of the process of assigning staff positions to UNA, there are more than 10 staff who have different positions. Furthermore, the data is processed using the profile matching method. The processing stage is through the selection of criteria and then it is processed to get the results of the analysis. Followed by calculating the level of accuracy with the results of the analysis of the UNA staffing section. The results of the testing of this method were that there were 16 staff who were very suitable with their expertise, while 28 people were deemed suitable and 6 more staff were not. So that the level of accuracy is 88%. The assessment of the test results has been able to provide an assessment of the appropriate position. but it can already be recommended to assist the staffing of UNA in analyzing the positions that are currently running to improve the performance of staff in the UNA environment.
Combination of Regression and ARIMA Methods ( Reg – ARIMA ) Stock Price Prediction Model Br. Sinulingga, Wita Oktaviana; Purba, Ronsen; Fermi Pasha, Muhammad
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.v7i1.5474

Abstract

This research is motivated by the limitations of the ARIMA method, which is only suitable for short-term forecasting and specific periods. Therefore, a combination of Regression and ARIMA methods (Reg- ARIMA) is introduced to predict stock prices over a longer period. The purpose of this study is to implement a combination of Regression and ARIMA methods to build a stock price prediction model. The research methodology involves using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) to measure the accuracy of the generated prediction model. The study results indicate significant variations in MAPE and RMSE values among different stocks, reflecting the performance and liquidity of those stock markets. For example, stocks such as ITMG and UNTR show strong performance, while stocks with low closing values may carry higher risks or slower growth. In conclusion, the Reg-ARIMA combination method is effective in extending the range of stock price forecasting, providing a more accurate alternative compared to using only the ARIMA method. This suggests that this hybrid approach can be used to enhance investment decision-making strategies in the stock market.

Page 78 of 80 | Total Record : 795

 76   77   78   79   80 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025 Vol. 7 No. 2 (2025): Forthcoming: 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