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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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User Interface Design Prototype Application Special Onthel Bicycle Tourism in Towilfiets Yogyakarta Yulianto, Arief
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.v6i1.3565

Abstract

Foreign tourist visits to Yogyakarta, Indonesia have increased in 2022 and 2023 after Covid-19. Many tourists are seeking unique experiences, such as riding on bicycles to enjoy the beautiful scenery and interact with local residents. Towilfiets, a pioneer in onthel bicycle tourism, has been operating in Bantar Hamlet, Kulon Progo for around 10 years. With the growing demand for this activity, Towilfiets needed to innovate their promotion methods, specifically in the digital industry. The development of a user-interface design-based application became crucial to enhance and facilitate the onthel bicycle tourism experience at Towilfiets. The research conducted used a mixed method approach with a phenomenological qualitative method to gather interview data. The prototype method was chosen to allow for intensive and better communication between developers and users. The validation of the questionnaire data was calculated using the Scalable Usage System and received a good score 75 up to score 100 point, indicating acceptable usability. By focusing on user needs and the unique characteristics of tourist destinations, this application aims to increase user engagement and provide relevant and useful information about bicycle tourist attractions in the area. Ultimately, the research aims to develop an innovative and contextualized user interface design application that supports the growth of onthel bike tourism in Towilfiets, located in Dusun Bantar, Kulon Progo, Yogyakarta, Indonesia.
Design a Desktop-Based Load and Customer Calculation Application Information System (SIAPEL) Wahyuni, Asri; Zaman, Dzoen Nuraeni Badarul
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.v6i1.3570

Abstract

In today's technological developments, many people are using technology to make work easier, as is PT. PLN Persero Customer Service Implementation Unit (UP3) Tasikmalaya. Several parts of this company, especially the section for recording expenses and customers by the PDKB Team, still use manual methods, namely by calculating using a calculator. This method is very risky, especially as it has the potential for errors in calculations or writing of the recorded numbers. Given these problems, a desktop-based load and customer calculation application information system (SIAPEL) was built. Information system solutions for related load and customer data calculation applications (SIAPEL) so that the results obtained are faster and more accurate. By making direct observations or observations, actively communicating with related fields through the interview process, and looking for research materials that support building an application as a solution to the problems faced. The load and customer calculation application information system (SIAPEL) is an application that can calculate load and customer data and can store the data as a form of company archive. The system development used is waterfall with stages or processes carried out sequentially from the system. The software used to build the load and customer calculation application information system (SIAPEL) is NetBeans 8.2, Java Development Kit 1.8, and MySql. Users can process load calculation data and process customer calculation data. And users can print reports from data that has been entered into the system database. With SIAPEL, it is hoped that it can reduce the risk of information errors and make it easier for users to calculate, store and process data. And it can be used as a more effective way to process data compared to using manual methods
Enhancing Multi-Layer Perceptron Performance with K-Means Clustering Pardede, Doughlas; Ichsan, Aulia; Riyadi, Sugeng
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.v6i1.3600

Abstract

Machine learning plays a crucial role in identifying patterns within data, with classification being a prominent application. This study investigates the use of Multilayer Perceptron (MLP) classification models and explores preprocessing techniques, particularly K-Means clustering, to enhance model performance. Overfitting, a common challenge in MLP models, is addressed through the application of K-Means clustering to streamline data preparation and improve classification accuracy. The study begins with an overview of overfitting in MLP models, highlighting the significance of mitigating this issue. Various techniques for addressing overfitting are reviewed, including regularization, dropout, early stopping, data augmentation, and ensemble methods. Additionally, the complementary role of K-Means clustering in enhancing model performance is emphasized. Preprocessing using K-Means clustering aims to reduce data complexity and prevent overfitting in MLP models. Three datasets - Iris, Wine, and Breast Cancer Wisconsin - are employed to evaluate the performance of K-Means as a preprocessing technique. Results from cross-validation demonstrate significant improvements in accuracy, precision, recall, and F1 scores when employing K-Means clustering compared to models without preprocessing. The findings highlight the efficacy of K-Means clustering in enhancing the discriminative power of MLP classification models by organizing data into clusters based on similarity. These results have practical implications, underlining the importance of appropriate preprocessing techniques in improving classification performance. Future research could explore additional preprocessing methods and their impact on classification accuracy across diverse datasets, advancing the field of machine learning and its applications
K-Nearest Neighbor Algorithm and Case Base Reasoning on Xenia Car Damage Detection Expert System Ananda, Bella; Putri, Raissa Amanda
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.v6i2.3700

Abstract

PT Astra Daihatsu Motor or commonly abbreviated as ADM is the Sole Agent Brand Holder (ATPM) of Daihatsu cars in Indonesia. Xenia car is one of the most popular cars in Indonesia. Although there are many Xenia car users, it is not uncommon for Xenia cars to experience damage caused by the ignorance of car users, where the car user only knows how to use it but does not know how to maintain the car properly and correctly. Before damage occurs to the car, the car usually experiences several symptoms of damage that the user does not realize. With that, there are often difficulties experienced by users to find out the type of damage to the car. The purpose of applying and designing applications in this study is to apply the Case Based Reasoning method with the K-Nearest Neighbor Algorithm to detect damage to Xenia cars and to design and build applications with the Case Based Reasoning method with the K-Nearest Neighbor Algorithm to detect damage to web-based Xenia cars. This research uses the Research and Development method. Based on the results of research from previous cases, the new case has similarities with 5 cases and the highest similitude value is with the highest type, namely the type of Injector Malfunction damage with a value of 0.625 or around 62.5%. This expert system application can detect and determine the results of Xenia car engine damage detection by applying a method that looks for the closest similarity value of new cases to old cases, namely the Case Based Reasoning method and the K-Nearest Neighbor Algorithm looking for the closest neighbors of the same weight value. This web-based expert system can be used by users to find the results of Xenia Car engine damage detection experienced by determining the symptoms that are available in web-based applications. This web-based application can also provide solutions from the detection results of the type of Xenia car engine damage.
Integrating Augmented Reality and Simulation Game for Flower Board Design Pratiwi, Eka; Triase, Triase; Sinaga, Imam Adlin
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.v6i2.3712

Abstract

Augmented Reality was regarded as one of the technologies that merged the real world with the virtual one. Its development was carried out by developers across various fields, including businesses such as floral services, exemplified by Berkah Florist. In practical application, Berkah Florist encountered challenges related to efficiency and customer satisfaction in the floral design process. The prevailing methods, such as displaying photographs or employing paper-based designs, were time-consuming and susceptible to paper damage, thus hindering customers from expressing their preferences accurately and disrupting the design process. To address these challenges, an application was developed to streamline the floral design process, aiming to make it more appealing. This research aimed to assist customers and streamline Berkah Florist's operations by facilitating the modeling and visualization of more captivating and effective designs. The application, based on a simulation game, was developed using Research and Development (R&D) and Rapid Application Development (RAD) methodologies. The application presented floral designs through an AR-enabled camera, replicating real-world conditions. The incorporation of Augmented Reality in the application garnered interest and engagement from prospective customers while alleviating boredom. Designed to provide a delightful experience for potential customers, the application aimed to enhance their interest in reusing it. Consequently, Berkah Florist could enhance customer experience and improve efficiency in the floral design process.
Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies Aprijal, Rendi; Siregar, Iqbal Wiranata; Siahaan, Andysah Putera Utama; Marlina, Leni
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.v6i2.3723

Abstract

In the digital era, manufacturing industries confront challenges like heightened global competition and intricate production processes, urging them to boost efficiency and productivity. Amidst these circumstances, Big Data emerges as a pivotal opportunity to enhance manufacturing performance. Big Data, characterized by vast volumes of data, utilizes advanced data mining to machine learning techniques for analysis. Data analytics, an interdisciplinary field, profoundly impacts manufacturing operations, enabling deeper insights into production processes. By analyzing production data, companies identify inefficiencies, streamline workflows, and enhance operational efficiency and productivity. Predictive maintenance through sensor data analysis prevents machine failures, while logistics data analysis optimizes supply chains and inventory management, reducing costs and enhancing competitiveness. However, implementing Big Data analytics presents challenges such as rapid data growth, diverse data sources, real-time insights, skill shortages, and data fragmentation. Overcoming these hurdles requires robust technology, skilled personnel, and effective data management strategies. Examples of Big Data analytics applications include customer behavior analysis by Amazon and Netflix, fraud detection in insurance, and urban mobility optimization. Success factors in data analytics implementation include effective data-driven communication, technology integration, and skill enhancement. In conclusion, implementing Big Data Analytics in manufacturing promises significant benefits in operational efficiency, product quality, and competitiveness. Overcoming challenges necessitates robust strategies and consideration of ethical and security issues, ensuring responsible data usage. With a deep understanding of Big Data Analytics, manufacturing companies can leverage this technology to achieve higher efficiency and competitiveness in the global market.
Implementing Distribution Requirement Planning in Medan City Health Department's Medicine Distribution System Nuha, Salsabila Isnain; Suendri, Suendri; Harahap, Aninda Muliani
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.v6i2.3781

Abstract

One of the pharmacy installations located in the Medan city area was tasked with overseeing the management of pharmaceutical inventory for public health facilities, ensuring adequate stock levels, and processing medication-related data, including receiving supplies and LPLPO forms from 41 Public Health Centers. Supervisors at the pharmacy installation were responsible for dispensing medications, while medication managers at the Public Health Centers handled medication requests by completing LPLPO forms and sending them to the installation. Issues arose regarding the accuracy of medication data within its operations, encompassing aspects such as initial stock, receipt of medications, inventory management, medication disbursement (including usage, damaged, or expired items), remaining stock, medication requests, and discrepancies between reported and actual medication quantities. The objective of this study was to establish a web-based data processing system utilizing the Distribution Requirement Planning (DRP) methodology. The DRP approach offered significant insights for forecasting medication stock demands and effectively guided the pharmacy installation in meeting the medication needs of the Public Health Centers. Furthermore, the DRP method shed light on the distribution process costs, thus serving as a valuable tool for enhancing cost efficiency and effectiveness. Results obtained through the DRP approach provided a more efficient distribution process, yielding a notable 93% reduction in expenditure. Additionally, the DRP method successfully anticipated future requirements by employing structured calculations that delineated demand levels experienced by each Public Health Center, accounting for the distinct needs of each facility.
Analysis of Logistic Regression Regularization in Wild Elephant Classification with VGG-16 Feature Extraction Ichsan, Aulia; Riyadi, Sugeng; Pardede, Doughlas
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.v6i2.3789

Abstract

The research article explores the intersection of image-based wildlife classification and logistic regression regularization, focusing on the classification of wild elephant species. It begins by highlighting the significance of ecological research in biodiversity monitoring and conservation and introduces Convolutional Neural Networks (CNNs) as potent tools for feature extraction from images. The VGG-16 model is particularly emphasized for its ability to capture hierarchical representations of visual features crucial for classification tasks. The integration of VGG-16 feature extraction with logistic regression regularization is proposed as a compelling approach, offering a balance between sophisticated feature representation and efficient classification algorithms. The literature review delves into image-based wildlife classification, emphasizing the role of CNNs, especially VGG-16, in extracting discriminative features. It discusses the fusion of VGG-16 features with logistic regression and the challenges in this field, such as dataset annotation and environmental variability. The method section outlines the dataset acquisition, feature extraction using the VGG-16 architecture, and model configuration using logistic regression with lasso and ridge regularization. The process of finding the optimal regularization parameter (lambda) and model evaluation through cross-validation is detailed. Results showcase the optimal lambda values for lasso and ridge regularization and compare the performance of logistic lasso and logistic ridge models. Misclassification analysis reveals factors influencing classification accuracy, including feature variability and contextual complexity. The discussion reflects on the implications of the findings, emphasizing the importance of lambda selection and addressing challenges in wildlife classification. It suggests avenues for further research, such as advanced modeling techniques and feature engineering approaches. In conclusion, the study contributes to advancing wildlife classification efforts by leveraging state-of-the-art techniques and sheds light on opportunities to enhance classification accuracy in wildlife conservation.
Analysis of Gradient Boosting, XGBoost, and CatBoost on Mobile Phone Classification Agus Fahmi Limas Ptr; Siregar, Muhammad Mizan; Daniel, Irwan
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.v6i2.3790

Abstract

In the ever-evolving landscape of mobile phone technology, accurately classifying device specifications is paramount for market analysis and consumer decision-making. This research conducts a comprehensive analysis of mobile phone specification classification using three prominent machine learning algorithms: Gradient Boosting, XGBoost, and CatBoost. Through meticulous dataset acquisition and preprocessing steps, including resolution normalization and price categorization, features essential for classification analysis were standardized. Robust cross-validation techniques were employed to assess model performance effectively. The study demonstrates the significant impact of normalization techniques on improving model performance across all algorithms and fold variations. CatBoost consistently emerges as the top-performing algorithm, followed closely by XGBoost, with Gradient Boosting displaying respectable performance. Notably, CatBoost consistently achieves the highest AUC values and accuracy scores, demonstrating superior performance in accurately classifying mobile phone specifications. These findings underscore the importance of preprocessing methods and algorithm selection in achieving optimal classification results. For mobile phone manufacturers, leveraging machine learning algorithms for effective classification can inform product development strategies, optimizing offerings based on consumer preferences. Similarly, for data analysts, employing appropriate preprocessing techniques and algorithmic approaches can lead to more accurate predictions and informed decision-making. Future research avenues include exploring advanced preprocessing methods, investigating alternative algorithms, and incorporating additional features or datasets to enrich the classification process. Overall, this research contributes to understanding mobile phone specification classification through machine learning methodologies, offering actionable insights for industry practitioners and researchers to address evolving market dynamics and consumer preferences.
Implementation of User Experience Design Approach in Web Based E-Commerce for the Agricultural Sector Saprida, Saprida; Putri, Raissa Amanda; Harahap, Aninda Muliani
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.v6i2.3809

Abstract

The technological advancements of the past have transformed various sectors, including information, education, and commerce. Many utilized the internet to enhance business and trade efficiency. Pantai Gading Village was a significant contributor to agricultural production. Its residents traditionally sold agricultural products locally, resulting in a narrow market scope. Consequently, a web-based E-commerce platform was developed using the User Experience Design Process to aid farmers and expand the market for agricultural products in the village. E-commerce facilitated cost reduction for companies, consumers, and management while enhancing service quality and speed. Through this platform, farmers could promote and sell their products online, overcoming the limitations of the local market and enhancing the village's global visibility. User Experience Design (UXD) improved user satisfaction with products through enhanced usability, accessibility, and satisfaction in interactions. This approach yielded designs that were neat, simple, intuitive, flexible, and appealing, providing users with a unique experience and differentiating products or services from competitors. The author of this study employed the Research and Development (R&D) methodology and the Waterfall development method. The system developed incorporated user experience design processes derived from questionnaire results. Users expressed the need for features such as live chat for each product, shipping options, displaying reviews, and offering Cash on Delivery payment method. This system facilitated and streamlined the marketing of agricultural products, thus boosting sales in Pantai Gading Village.

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