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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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The Impact of Big Data on Enterprise Architectural Design: A Conceptual Review Ira Diana Sholihati; Bayu Yasa Wedha; Sari Ningsih; Ratih Titi Komala Sari
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3449

Abstract

A conceptual analysis of the impact of big data on enterprise architecture design is provided in this article. Within the framework of expanding digitalization, big data has emerged as a pivotal component in delineating the strategy and framework of organizations. The objective of this study is to investigate the ways in which big data can impact and facilitate the growth of efficient enterprise architecture. Qualitative analysis is the method utilized by researchers to comprehend the intricacies of the interaction between enterprise architecture and big data. This article examines several facets by conducting an extensive review of the literature, including the ways in which big data can facilitate the enhancement of analytical capabilities, innovation in business processes, and strategic decision-making. Emerging challenges, including data security, privacy, and the necessity for IT infrastructure adaptation, are also considered in this study. The outcomes of the review indicate that the implementation of big data in enterprise architecture may substantially alter business strategies and operations. These encompass enhanced system adaptability, customized service provision, and predictive functionalities. Nonetheless, these modifications necessitate modifications to privacy policies, risk management, and data governance. This study presents novel findings regarding the influence of big data on enterprise architecture and provides researchers and practitioners with recommendations for developing and executing successful big data strategies. This research thereby enhances the current body of literature and offers practical guidance in the field.
Development of Web-Based Student Registration Information System with Rapid Application Development Approach Arif Tri Widiyatmoko; Agung Nugroho; Wiyanto Wiyanto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3459

Abstract

The management of student data and the student registration process is an important aspect in the world of education. In the digital era, the use of information technology is crucial to maintain the quality and efficiency of education. Therefore, the development of a web-based student registration information system with a Rapid Application Development (RAD) approach is an efficient and effective solution. This research proposes the development of a web-based student enrolment information system with a RAD approach to improve efficiency, accessibility of student data, and the ability to adapt the system to continuous change. The RAD method consists of requirements planning stages, RAD design workshops, and implementation. The test results of the application show that this application is worth using and meets the expected standards. Thus, the development of a web-based student registration information system with the RAD approach is expected to provide innovative and efficient solutions in overcoming student data management problems and the student registration process
Integration Of Open CV LBF Model To Detect Masks In Health Protocol Surveillance Systems Yovi Litanianda; Moh Bhanu Setyawan; Adi Fajaryanto C; Ismail Abdurrozzaq Z; Charisma Wahyu Aditya
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3460

Abstract

The Corona Viruses Diseases pandemic that was rife in early 2020 and hit many countries caused discipline to be applied to health protocols. The prevention of physical contact between humans gave rise to new traditions in aspects of human life. Almost all public facilities in Indonesia require visitors to wear masks as a means of preventing exposure to viruses in the air. However, this advice is often ignored by some people. In addition to endangering many people, this condition also makes public facility managers need extra resources in the form of time, energy and costs to ensure this health protocol is implemented. The existence of these problems triggers the emergence of innovations to present a system that provides assurance and convenience in ensuring compliance with health protocols for the use of masks through creative and effective methods. This method is done by utilizing CCTV cameras or webcams at the entrance equipped with an Artificial Intelligence program designed to be able to detect the use of masks on visitors to public facilities, and without the need for other sensors. The detection system is built on the concept of facial biometrics and utilizes the OpenCV LBF model to detector landmarks on a person's face. Based on tests conducted through several scenario, it can be said that the open CV LBF model successfully identified the use of masks within 35 seconds, increasing the reading distance to 2 meters making the process longer. In addition, in indoor lighting conditions, the system experienced 1 detection error with a process time of 18 seconds, while for well-light outdoor conditions the system managed to detect all objects within 10 seconds.
Comparative Analysis of Machine Learning Algorithms for Detecting Fake News: Efficacy and Accuracy in the Modern Information Ecosystem Gregorius Airlangga
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3466

Abstract

In an era where the spread of fake news poses a significant threat to the integrity of the information landscape, the need for effective detection tools is paramount. This study evaluates the efficacy of three machine learning algorithms—Multinomial Naive Bayes, Passive Aggressive Classifier, and Logistic Regression—in distinguishing fake news from genuine articles. Leveraging a balanced dataset, meticulously processed and vectorized through Term Frequency-Inverse Document Frequency (TF-IDF), we subjected each algorithm to a rigorous classification process. The algorithms were evaluated on metrics such as precision, recall, and F1-score, with the Passive Aggressive Classifier outperforming others, achieving a remarkable 0.99 in both precision and recall. Logistic Regression followed with an accuracy of 0.98, while Multinomial Naive Bayes displayed robust recall at 1.00 but lower precision at 0.91, resulting in an accuracy of 0.95. These metrics underscored the nuanced capabilities of each algorithm in correctly identifying fake and real news, with the Passive Aggressive Classifier demonstrating superior balance in performance. The study's findings highlight the potential of employing machine learning techniques in the fight against fake news, with the Passive Aggressive Classifier showing promise due to its high accuracy and balanced precision-recall trade-off. These insights contribute to the ongoing efforts in digital media to develop advanced, ethical, and accurate tools for maintaining information veracity. Future research should continue to refine these models, ensuring their applicability in diverse and evolving news ecosystems.
Spreadsheet-Based Car Engine Temperature And Compression Pressure Gauge Wijaya Al Hadad Sudjono Putra; Jamaaluddin Jamaaluddin; Izza Anshory; Akhmad Ahfas
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3472

Abstract

Measuring the engine's temperature and compression pressure in cars is a crucial part of engine maintenance and performance evaluation. The objective of this study is to develop an accurate and efficient spreadsheet-based measurement system for determining engine temperature and compression pressure in automobiles. For this investigation, data from car engines was collected using engine temperature thermometers and compression pressure gauges. The collected data is then fed into a spreadsheet designed specifically to assess and automatically calculate engine temperature and compression pressure. The results show how reliable and accurate the measurements made using this spreadsheet-based measurement technique are. This approach's main benefits are also its flexibility and ease of use, since users can rapidly adapt the spreadsheet to the particular needs of the engine being tested. With this technology in place, car owners or professionals who do engine maintenance can quickly detect potential engine problems, such as valve leakage or unexpected temperature increases. Therefore, to increase fuel efficiency and extend engine life, spreadsheets can be utilized as a tool for measuring engine performance. The goal of this project is to facilitate the work of auto repair shop owners by utilizing the Esp8266 microcontroller for the DS18b20 sensor and compression sensor. Even so, there are several difficulties, like a sluggish internet connection that delays uploading data to the LCD and spreadsheets that are less useful because of a bad internet connection.
Analysis of the Level of Satisfaction with the Use of Computer Assisted Test at SMAN 1 Parang Magetan Aliyadi; Jamilah Karaman
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3473

Abstract

The use of technology in education sector provides many conveniences in carrying out learning activities. One use of technology in the education sector can be used by teaching staff to carry out evaluations after carrying out a series of learning activities. The technology that can be used can be in any form, one of which is a system that is built to facilitate the learning process from start to finish. One system that can be used is the systemComputer Assisted Test (CAT). CAT is a system that uses computers as the main medium. In the CAT system, practice questions can be input which will be filled in by students later. This CAT system is used by students to work on practice questions. The system that has been used needs to be evaluated to determine its advantages and disadvantages. One method that can be used to analyze a system is the usability approach User Satisfaction Quesionnaire (UEQ). This approach uses user experience as the main aspect in system assessment and there are 6 variables assessed, namely Attractiveness (Daya Tarik), Efficiency (Efisiensi), Clarity (Tingkat Kejelasan), Dependability (Ketepatan), Stimulation (Stimulasi), and Novelty (Kebaruan). This research uses a UEQ questionnaire which will be filled in by 88 students and 12 teachers who act as research samples after working on practice questions through the CAT system. The completed questionnaire was then analyzed and it was discovered that for the Attractiveness, Efficiency, Clarity and Stimulation variables received the "Very Good" criteria, the Novelty variable "Good", and  the Dependability variable "Below Average".
Decision Support System for the Presidential Election of the Student Executive Board Using the Multi-Factor Evaluation Process Method Suryani Suryani; Annah Annah; Nurdiansah Nurdiansah; Faizal Faizal; Akbar Bahtiar; Hardi Hardi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3480

Abstract

Student Executive Board is a student organization or executive institution found in every tertiary institution and can represent the existence of a tertiary institution. The student Executive Board is headed by the Student Executive Board President, who is assisted by the Secretary-General, three Coordinating Ministers, and several ministries that represent student needs. In carrying out its function as a forum for student aspirations to make changes (agents of change) in paradigm, emotional, intellectual, and religious values, the Student Executive Board requires student candidates who are in synergy with their vision and mission. The Student Executive Board Presidential Election is usually held every year. A decision support system (DSS) was built by implementing the Multi-Factor Evaluation Process (MFEP) method to easily and efficiently elect the Student Executive Board President quickly and efficiently. The criteria used are communication skills (C1), leadership attitude (C2), vision and mission (C3), skills (C4), and organizational experience (C5). Each criterion is weighted where the total weight of all criteria equals 1. Next, calculate the evaluation weight value (EW), total evaluation weight (TEW), and ranking. The research results show that alternative 7 (A7), with the highest score of 4.35, is the student candidate with the top ranking, meaning A7 is the most recommended to be elected as President of the Student Executive Board. With this DSS, we can provide appropriate recommendations for Student Executive Board Presidential candidates and assist universities and students in carrying out the selection process quickly and efficiently.
Analysis of Machine Learning Classifiers for Speaker Identification: A Study on SVM, Random Forest, KNN, and Decision Tree Gregorius Airlangga
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3487

Abstract

This study investigates the performance of machine learning classifiers in the domain of speaker identification, a pivotal component of modern digital security systems. With the burgeoning integration of voice-activated interfaces in technology, the demand for accurate and reliable speaker identification is paramount. This research provides a comprehensive comparison of four widely used classifiers: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT). Utilizing the LibriSpeech dataset, known for its diversity of speakers and recording conditions, we extracted Mel-frequency cepstral coefficients (MFCCs) to serve as features for training and evaluating the classifiers. Each model's performance was assessed based on precision, recall, F1-score, and accuracy. The results revealed that RF outperformed all other classifiers, achieving near-perfect metrics, indicative of its robustness and generalizability for speaker identification tasks. KNN also demonstrated high performance, suggesting its suitability for applications where rapid execution and interpretability are critical. Conversely, SVM and DT, while yielding moderate and lower performances respectively, highlighted the necessity for further optimization. These findings underscore the effectiveness of ensemble and distance-based classifiers in handling complex patterns for speaker differentiation. The study not only guides the selection of appropriate classifiers for speaker identification but also sets the stage for future research, which could explore hybrid models and the impact of dataset variability on performance. The insights from this analysis contribute significantly to the field, providing a benchmark for developing advanced speaker identification systems
Spreadsheet-Based Automatic Print Cost Calculator Mokhamad Ariel Fadilah; Izza Anshory; Jamaaluddin Jamaaluddin; Agus Hayatal Falah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3492

Abstract

Production cost calculation is a crucial factor that impacts productivity and profitability in the printing industry. This study presents an automatic print costing tool that runs on a spreadsheet and is intended to increase operational efficiency and calculation accuracy in Small and Medium-Sized Printing Enterprises (SMEs). With the use of Esp32 as a microcontroller, TCS3200 and infrared sensors, this project seeks to create an automated print pricing spreadsheet that would help printing SMEs swiftly and precisely determine product prices. This tool's components include an LCD to show the cost computation findings, an infrared sensor to identify the number of printed sheets, a TCS3200 sensor to determine whether or not colored paper is present, and a push button feature to resume the calculation and upload the entire cost to the spreadsheet. The instrument functions effectively and aids users in doing cost calculations in an efficient manner, according to the findings. Despite some challenges, such as slow internet connections that cause delays, to enable effective cost assessment poor internet connection. This strategy should decrease calculation errors and increase manufacturing cost management effectiveness.
Home Surveillance Monitoring with Esp32-Cam and SD Card For Data Storage Ivan Danu Tirta; Arief Wisaksono; Akhmad Ahfas; Jamaaluddin Jamaaluddin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3498

Abstract

In the last three years the crime rate of theft has increased, to reduce crime this form of theft can be overcome by making a home security system using ESP32-CAM, the system made aims to conduct surveillance that can be seen again the results of images that have been taken by ESP32-CAM, then stored on the SD Card and send notifications to social media. This research uses the R&D (Research &; Development) method or research, and development is a systematic study process to develop and validate products to be used in education. Products developed / produced include training materials for teachers, teaching materials, learning media, questions, and management systems in learning. The result of the implementation of a security system is the stage where the system that has been designed explains the creation of a system in accordance with previous analysis and design. After the implementation stage is carried out, a system test is needed to prove that the application can run properly. The test results that have been done using the android application and Sdcard run well, the PIR sensor can only detect objects as far as 4 meters. With this system, it is expected to be able to provide protection and security for homes, property, and residents. On the other hand, this approach also creates a chance to dig deeper into technology development using ESP32-CAM as an effective and efficient solution to tackle rising crime.

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