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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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Case Study: Gradient Boosting Machine vs Light GBM in Potential Landslide Detection Djarot Hindarto
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.v6i1.3374

Abstract

An increasing demand for precise forecasts concerning the likelihood of landslides served as the impetus for this investigation. Human life, infrastructure, and the environment are all profoundly affected by this natural occasion. Constructing models capable of discerning intricate patterns among diverse factors that impact the likelihood of landslide occurrences constitutes the primary obstacle in landslide detection. Predicting potential landslides requires algorithms that are both accurate and efficient in their processing of vast quantities of data encompassing a variety of geographical, environmental, and ecological characteristics. An evaluation of the efficacy of both Gradient Boosting Machine and Light Gradient Boosting Machine in identifying patterns associated with landslides is accomplished by comparing their performance on a large and complex dataset. In the realm of potential landslide detection, the primary aim of this research endeavor is to assess the predictive precision, computation duration, and generalizability of Gradient Boosting Machine and Light Gradient Boosting Machine. This research aims to enhance comprehension regarding the comparative benefits of these two approaches in surmounting the obstacles associated with risk assessment and modeling pertaining to potential landslides, with a specific emphasis on efficiency and precision. The research findings are anticipated to serve as a valuable reference in the identification of more efficient approaches to reduce the likelihood of landslide-induced natural catastrophes. The accuracy of the GBM experiment reached 82% and LGBM reached 81%.
Web-based Ukp Public Health Center Services System Using the Waterfall Method Fatmariani Fatmariani; Andri Saputra; Lindia Guspita 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.v6i1.3380

Abstract

Basic health services for the community are community health centers which have Health Service Units (UKP). UKP provides general health services to the community, which has many polyclinics and is interconnected with doctors, patients and administration. So far, in processing service data, there have been difficulties in general polyclinic units when receiving patient information, which is still done by recording it in a book, so there are often errors in information in patient registration services that should be received by the polyclinic that corresponds to the target polyclinic. The storage of patient data based on poly is not yet organized because the files that are stored and archived do not exist in each unit, so that when presenting data and searching you have to confirm who is archiving it, making it difficult for the data or information service department and services to be hampered. The method used in this research is the Waterfall method. This service system uses the waterfall method. The service system provides benefits in inputting and presenting data, searching for patient data such as registering online, then checking medical records to go to the clinic, online medical record results and viewing prescription information, then doctors can meet patients who carry out examinations. This system can provide good benefits and increase effectiveness and efficiency in health services for the surrounding community.
Comparing Neural Networks, Support Vector Machines, and Naïve Bayes Algorhythms for Classifying Banana Types Abwabul Jinan; Manutur Siregar; Vicky Rolanda; Dede Fika Suryani; Abdul Muis
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.v6i1.3381

Abstract

One of the most significant fruits for human consumption is the banana. Fruit consumption not only promotes health but also lowers the risk of heart disease, stroke, digestive issues, hypertension, some cancers, cataracts in the eyes, skin ailments, cholesterol reduction, and, perhaps most importantly, boosts immunity.The study included secondary data, which is information gathered from online resources like Kaggle. Ten categories of bananas will be identified from the 531 total varieties of bananas used as a train dataset: Ambon bananas, Stone bananas, Cavendish bananas, Kepok bananas, Mas bananas, Red bananas, plantains, Milk bananas, Horn bananas, and Varigata bananas. The development of information technology for image object recognition has become a very intriguing topic along with the rapid advancement of society, and it is undoubtedly directly tied to information data. In order to examine Naive Bayes, Support Vector Machine, and Neural Network techniques for classifying banana types, researchers will use the SqueezeNet Deep Learning model to extract features from photos. The study's findings will provide empirical evidence for the distinctions between each algorithm's accuracy, recall, and precision. Based on the collected results, the Neural Network (NN) method is the best in terms of classification, with accuracy of 72.3%, precision of 72.1%, and recall of 72.3%.
Utilizing Convolutional Neural Network for Learning Web-Based Braille Letter Classification System Ahmad Ridwan; Yoan Purbolingga; Hanisah Hanisah
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.v6i1.3386

Abstract

This paper aims to facilitate prospective teachers and people who want to learn braille letters. The system designed is a website that will classify braille letters using the convolutional neural network (CNN) method with the activation functions used, namely ReLU and Softmax. In this research, the input is an image of braille letters with grayscale elements. The output of the data is a regular alphabet letter. Most of this research data consists of training and testing data, which is 2,722 pieces. The accuracy results obtained in the data training process using Max Pooling and epoch 30 for data is 92.15%, epoch 50 is 94.58%, and for training data with epoch 100 is 96.64%. The test results using the system produce an accuracy value of all braille letter image data of 92.30%. Furthermore, for better system development, it is recommended to use hyperparameter tuning to minimize classification uncertainty in braille letter images.
Exploration of Artificial Intelligence (AI) Application in Higher Education: A Research Study in Kolaka, Southeast Sulawesi Sulfikar Sallu; Raehang Raehang; Qammaddin Qammaddin
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.v6i1.3396

Abstract

This article explores the implementation of Artificial Intelligence (AI) in higher education in Kolaka, Southeast Sulawesi. The research aims to identify and analyze how AI can be used to enhance the learning process and academic administration in universities. The research method includes primary data collection through interviews and observations, as well as secondary data from academic sources. The results show that AI contributes significantly to operational efficiency and the learning experience. This research provides new insights into the application of AI in the academic environment and offers recommendations for higher education institutions that want to integrate AI technology
Optimizing Transportation Services: Using TOGAF for Efficiency and Quality Bayu Yasa Wedha; Djarot Hindarto
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.v6i1.3407

Abstract

In the rapidly expanding transportation industry, it is crucial to make focused and coordinated efforts to improve services with maximum efficiency. This paper seeks to explore the optimization of the Enterprise Architecture approach to effectively attain the primary objectives of the transportation industry, specifically the enhancement of service quality. The main emphasis is on implementing the enterprise architecture methodology of the open group architecture framework on a strategic basis. This paper examines how Enterprise Architecture can offer systematic and quantifiable solutions by identifying problems in infrastructure and operational processes. The research aims to provide comprehensive insights into how the Enterprise Architecture concept can optimize operational efficiency and streamline processes in the provision of transportation services. By implementing TOGAF, it is expected that the integration of systems will be seamless, technology usage will be optimized, and customer experiences will be improved. To summarize, this paper demonstrates the desire to improve transportation services. It explains how Enterprise Architecture methods, specifically within the TOGAF framework, can directly lead to advantages such as increased operational efficiency and improved service quality. This paper aims to be easily understood by a wide range of readers, including management, Information Technology professionals, and other stakeholders in the transportation industry. It avoids using overly technical language to ensure accessibility and comprehensibility.
Sentiment Analysis on Cyanide Case After 'Ice Cold' Aired with NLP Method using Naïve Bayes Algorithm Rahmatika Hizria; Sarwadi Sarwadi; Rabiatul Adawiyah Hasibuan; Ramadhani Ritonga; Rika Rosnelly
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.v6i1.3408

Abstract

Information technology is developing increasingly rapidly, and the reach of the Internet has expanded even to remote areas. The public increasingly uses social media as a source of information that discusses all aspects of people's lives. Social media has a vital role for most people, one of which is the news of the cyanide coffee case. The Cyanide Coffee case was discussed again by netizens after Netflix raised this case in a documentary film entitled Ice Cold, which made the public even more convinced of the irregularities of the case. Based on this, sentiment analysis is needed to extract comments to obtain public opinion information. The sentiment analysis aims to create a sentiment model to determine public comments on this case. Therefore, this research was conducted to find out and classify public sentiment on the Cyanide Coffee Case using the Natural Language Processing (NLP) method, which is a text preprocessing process followed by the tokenization stage. Data filtering was used using Indonesian Stopwords, and then normalization was continued using Porter Stemmer. In this study, data collection was carried out based on public comments on Ice Cold shows on the TikTok platform using TikTok Comments Scraper. The test results show that the classification using naïve Bayes obtained the results of 22 negative comments, 4052 neutral comments and 34 positive comments. The classification results of this study are 87% accuracy, 97.6% precision, 87% recall, and 91.9% F-Score.
Analysis of String Matching Application on Serial Number Using Boyer Moore Algorithm Dede A. Tarigan; Adiyanto O. Buaton; Briyandana Briyandana; Erica R. Safitri; Rika Rosnelly
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.v6i1.3410

Abstract

Nowadays, technology has become the most important pillar in business management. The rapid development of technology has a significant impact on various aspects of business, from operational efficiency to marketing strategies. Applications are very important in a company or agency. With an information system, companies and agencies can easily guarantee the quality of information that will be presented for decision-making. Now, much information can be easily obtained quickly, thanks to information technology. The speed and accuracy of information delivery is a challenge for all producers in running their business. Boyer-Moore algorithm is one of the algorithms that can be used in the Barcode Generator application to scan barcode product serial numbers. The Boyer-Moore algorithm method functions to find sequence numbers. The development process requires several stages of investigation in the form of data collection techniques, problem identification, application of the Boyer-Moore algorithm, implementation, and system testing. This iterative process makes the application of string matching with the Boyer-Moore algorithm technique into a very accurate application suitable for text search. This process is done by giving a pattern to the text. Therefore, the final result of string matching text search using the Boyer-Moore algorithm technique requires nine iterations. In the 9th iteration, the text and pattern conditions are matched or sequential. From the results of the manual computational search analysis work of applying the Boyer Moore string matching algorithm, several stages of the process are made, namely iterations 1 to 9, as a search step to determine string matches. In addition, patterns can be used with the number of shifts of patterns or text up to 13 times.
Implementation Convolutional Neural Network for Visually Based Detection of Waste Types Bayu Yasa Wedha; Ira Diana Sholihati; Sari Ningsih
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.v6i1.3427

Abstract

Waste detection plays an essential role in ensuring efficient waste management. Convolutional Neural Networks are used in visual waste detection to improve waste management. This study uses a data set that covers various categories of waste, such as plastic, paper, metal, glass, trash, and cardboard. Convolutional Neural Networks are created and trained with refined architecture to achieve precise classification results. During the model development stage, the focus is on utilizing transfer learning techniques to implement Convolutional Neural Networks. Utilizing pre-trained models will speed up and improve the learning process by enriching the representation of waste features. By using the information embedded in the trained model, the Convolutional Neural Network can differentiate the specific attributes of various waste categories more accurately. Utilizing transfer learning allows models to adapt to real-world scenarios, thereby improving their ability to generalize and accurately identify waste that may exhibit significant variation in appearance. Combining these methodologies enhances the ability to identify waste in diverse environmental conditions, facilitates efficient waste management, and can be adapted to contemporary needs in environmental remediation. The model evaluation shows satisfactory performance, with a recognition accuracy of about 73%. Additionally, experiments are conducted under authentic circumstances to assess the reliability of the system under realistic circumstances. This study provides a valuable contribution to the advancement of waste detection systems that can be integrated into waste management with optimal efficiency.
Online Tutoring's Technological Foundation and Future Prospects: Enterprise Architecture Development Sari Ningsih; Bayu Yasa Wedha; Ira Diana Sholihati
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.v6i1.3433

Abstract

This study examines the advancement of enterprise architecture with the objective of enhancing the technological infrastructure and long-term strategies in the online student tutoring sector. Online tutoring has emerged as the primary option for supporting the learning process in the rapidly advancing digital age. Identify the essential elements involved in establishing robust groundwork for an online tutoring platform, with a focus on highlighting the strategic significance of enterprise architecture. Examining the technological infrastructure that is customized to fulfill the demands of the tutoring sector constitutes the research methodology utilized in this investigation. Enterprise architecture serves as the fundamental framework that enables smooth integration among different systems, applications, and services used in online tutoring. Creating an enterprise architecture will subsequently generate a well-defined technology roadmap, empowering tutoring companies to innovate with greater precision. This architecture enhances the role of online tutoring in providing a more adaptable and personalized learning experience for students by utilizing advanced technologies like artificial intelligence and data analytics. This study emphasizes the significance of enterprise architecture in facilitating educational transformation and establishing a robust framework for online tutoring companies to progress efficiently. To foster the growth and advancement of the online tutoring industry, it is crucial to strategically enhance the technological infrastructure and implement a well-designed enterprise architecture. This will enable the sector to play a substantial role in shaping a dynamic and forward-thinking educational landscape.

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