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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 813 Documents
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Analysis of the Implementation of E-Learning in Melajah.id Using Human Organization Technology (HOT) Fit Model Pradhana, Gusti Komang; Dantes, Gede Rasben; Divayana , Dewa Gede Hendra
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2921

Abstract

This research is motivated by the absence of prior analysis on the success level of implementing Melajah.id e-learning and the performance analysis of the e-learning platform from the perspective of human, organizational, and technological support aspects. Therefore, the existence of this scholarly research is considered necessary as a consideration for stakeholders involved in the policy-making for the development of educational service quality in vocational schools. The purpose of this research is to assess the performance and success level of the implementation of the Melajah.id e-learning platform in use at SMK Negeri 3 Tabanan. For the indicators of the human variable, they include: (a) System Use and (b) User Satisfaction. For the indicators of the organizational variable, they include: (a) Organization Structure and (b) Organization Environment. As for the indicators of the technology variable, they encompass: (a) System Quality, (b) Information Quality, and (c) Service Quality. Data collection for the research was conducted through the distribution of online questionnaires using Google Forms, while supplementary data was obtained through observation and interviews to gather information that could not be revealed through questionnaires. Subsequently, the data analysis approach used to measure the success and performance of the e-learning system implementation was linear regression analysis as the quantitative method. Furthermore, qualitative data analysis was performed through content analysis of interview scripts and interpretation of observational data (photos and videos). The combination of the results from quantitative and qualitative analyses served as the basis for drawing conclusions and making recommendations regarding the analysis of the success of the Melajah.id e-learning implementation in the vocational school where the research was conducted.
Media Introduction to Aircraft Engine Collections Using QR Code at Nurtanio Laboratory ITD Adisutjipto Indrianingsih, Yuliani; Pratama, Berland Vardy; Aryanto, Salam; Nugraheny, Dwi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2946

Abstract

The Public Relations and New Student Admissions Center of the Adisutjipto Dirgantara Technology Institute often collaborates with high schools and vocational schools both from Java and outside Java in terms of campus visits and has a program called Campus Tour. In carrying out campus tour activities, there are several problems that often arise. Therefore, an application called Education for Aero Engineering Adisutjipto (TORERO) was created with a 360° virtual reality tour and voice over which was able to provide information about aircraft engines at the Nurtanio Laboratory using a QR Code. This application is expected to be able to help Public Relations and the New Student Admission Center at the Adisutjipto Aerospace Technology Institute. Application testing uses the Black Box method for system testing, and is carried out on several browsers with the results obtained that all browsers support the use of the TORERO application. User test results using the Likert scale method, obtained results with a success rate of 86.23% with a statement of strongly agree.
Integration Of Open CV LBF Model To Detect Masks In Health Protocol Surveillance Systems Litanianda, Yovi; Setyawan, Moh Bhanu; Fajaryanto C, Adi; Abdurrozzaq Z, Ismail; Aditya, Charisma Wahyu
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 Airlangga, Gregorius
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.
Analysis of the Level of Satisfaction with the Use of Computer Assisted Test at SMAN 1 Parang Magetan Aliyadi; Karaman, Jamilah
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 Airlangga, Gregorius
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
Bumdes Loan And Payment Apllication At The Bandar Telu Plantation Village Office Is Website-Based Awaliyana, Qurani; Ikhwan, Ali
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.v6i1.3508

Abstract

The aim of this research is to obtain a lending and payment application that can be applied to the Bandar Telu Plantation BUMDes. This research uses the RnD research method and the Waterfall development method. Data collection was carried out by means of observation, interviews and literature study. This results of this research show that BUMDes was established to help the community meet their needs by borrowing from the community. The websote-based application for borrowing and payment for the BUMDes Plantation Village in Bandar Telu produces an application that can be accessed by administrators or members. Where the management uses a website based system, and members use a mobile-based system that can be accessed via the internet and applications. With this application, it can make it easier for administrators and members to obtain information about ongoing loans and payments, so that there is no longer a need for general ledger recapitulation which is at risk of errors in calculating interest or accumulating the amount of members bills.
Prototype Of Moisture Content Meter In Grain Using Esp32 Based On Spreadsheet Ramadhan, Mochammad Derian; Wisaksono, Arief; Jamaaluddin, Jamaaluddin; Ahfas, Akhmad
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3530

Abstract

In the process period after the rice is harvested, the rice is then separated from the stalk and referred to as grain which will then be dried. The dried grain aims to reduce the water content, in measuring the water content of the grain, an effective and efficient measurement and database storage tool is needed for users to find out which grain is suitable for processing and can determine the quality of the water content of the grain. The method used in this research uses the RnD (Research And Development) method. In this test using capacitive soil moisture sensor and using database storage in the form of google spreadsheet. The capacitive soil moisture sensor is also calibrated with conventional measuring instruments (Grain Moisture Meter) to find out whether the sensor works properly and accurately. The results in this test found that all components are able to work properly and show an error value <1, the sample reading data will be sent to the database on google spreadsheet so that users can find out the data records in real time and detail.
Menu Sales Prediction at Kiyo Café Using Machine Learning Fitriana, Jesi; Triloka, Joko
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3556

Abstract

This research evaluates the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in predicting raw material stock for Café Kiyo. The study encompasses six key stages, including preparation, literature review, data collection, data mining processing, results and discussion, and conclusion with recommendations. The data mining process adheres to the Knowledge Discovery in Databases (KDD) framework, involving data selection, preprocessing, transformation, data mining, and interpretation and evaluation. The evaluation metrics reveal that KNN boasts a marginally higher accuracy of 98.71% compared to Naïve Bayes with 98.21%. KNN also demonstrates superior precision (81.25%) in identifying true positives, outperforming Naïve Bayes (72.59%). However, Naïve Bayes excels in recall, achieving 95.15% compared to KNN's 50.00%. The Area Under the Curve (AUC) analysis further confirms Naïve Bayes' superiority, with an AUC value of 0.995, indicating better performance in distinguishing between positive and negative classes.

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