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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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Integrating Augmented Reality and Simulation Game for Flower Board Design Eka Pratiwi; Triase Triase; Imam Adlin Sinaga
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 Rendi Aprijal; Iqbal Wiranata Siregar; Andysah Putera Utama Siahaan; Leni Marlina
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.
Implementation of K-Means Clustering in Recognizing Crime Hotspots and Traffic Issues Through GIS Aryo Pratama; Muhammad Dedi Irawan; Septiana Dewi Andriana
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.v6i2.3771

Abstract

The challenge of accurately identifying instances of crime and traffic issues has rendered the precise localization thereof difficult, thereby impeding the populace's access to information concerning areas of high risk and safety. Employing a Geographic Information System (GIS)-based mapping system utilizing the K-means clustering method, spatial data pertaining to crime and traffic concerns are grouped. The primary objective is to aid in the identification of high-risk areas concerning crime and traffic matters. The methodology employed in this study revolves around the application of the K-means clustering method to categorize spatial data relevant to crime and traffic issues. K-means clustering represents a non-hierarchical cluster analysis technique designed to partition data into multiple groups based on spatial similarities. Research findings elucidate that through the utilization of the K-means clustering method, three distinct sets of clusters predicated upon the intensity of crime and traffic issues emerge. Consequently, from these clustering outcomes, districts and specific locales falling within each cluster, denoted as moderately vulnerable (C1), vulnerable (C2), and highly vulnerable (C3), can be delineated. This system is poised to furnish recommendations to pertinent authorities for addressing areas exhibiting heightened intensity levels while concurrently facilitating the generation of reports and dissemination of information to the public via a dedicated website pertaining to areas at elevated risk of crime and traffic issues.
Implementing Distribution Requirement Planning in Medan City Health Department's Medicine Distribution System Salsabila Isnain Nuha; Suendri Suendri; Aninda Muliani Harahap
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 Aulia Ichsan; Sugeng Riyadi; Doughlas Pardede
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; Muhammad Mizan Siregar; Irwan Daniel
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; Raissa Amanda Putri; Aninda Muliani Harahap
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.
Evaluating the Efficacy of Machine Learning Models in Credit Card Fraud Detection Gregorius Airlangga
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.v6i2.3814

Abstract

This research evaluates the effectiveness of various machine learning models in detecting credit card fraud within a dataset comprising 555,719 transactions. The study meticulously compares traditional and advanced models, including Logistic Regression, Support Vector Machines (SVM), Random Forest, Gradient Boosting, k-Nearest Neighbors (k-NN), Naive Bayes, AdaBoost, LightGBM, XGBoost, and Multilayer Perceptrons (MLP), in terms of accuracy and reliability. Through a robust methodology involving extensive data preprocessing, feature engineering, and a 5-fold stratified cross-validation, the research identifies XGBoost as the most effective model, demonstrating a near-perfect mean accuracy of 0.9990 with minimal variability. The results emphasize the significance of model choice, data preparation, and the potential of ensemble and boosting techniques in managing the complexities of fraud detection. The findings not only contribute to the academic discourse on fraud detection but also suggest practical applications for real-world systems, aiming to enhance security measures in financial transactions. Future research directions include exploring hybrid models and adapting to evolving fraud tactics through continuous learning systems.
Comparative Analysis of Machine Learning Models for Credit Card Fraud Detection in Imbalanced Datasets Gregorius Airlangga
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.v6i2.3816

Abstract

This study presents a comprehensive evaluation of various machine learning models for detecting credit card fraud, emphasizing their performance in handling highly imbalanced datasets. We focused on three models: Logistic Regression, Random Forest, and Multilayer Perceptron (MLP), using a dataset comprising 555,719 transactions, each annotated with 22 attributes. Logistic Regression served as a baseline, Random Forest was evaluated for its high accuracy and low dependency on hyperparameter tuning, and MLP was tested for its capability to identify non-linear patterns. The models were assessed using ROC AUC, Matthews Correlation Coefficient (MCC), and precision-recall curves to determine their effectiveness in distinguishing fraudulent transactions. Results indicated that the Random Forest model outperformed others with a ROC AUC of 0.9868 and an MCC of 0.6638, showing substantial superiority in managing class imbalances and complex data interactions. Logistic Regression, although useful as a benchmark, exhibited limitations with a high number of false positives. MLP showed potential but was prone to a significant false positive rate, suggesting a need for further model refinement. The findings highlight the importance of choosing appropriate models and feature engineering techniques in fraud detection systems and suggest avenues for future research in real-time model deployment and advanced algorithmic strategies
Implementation of Statistical Quality Control Method in Product Quality Monitoring Information System Iqbal Maulana Syahputra; Triase Triase; Septiana Dewi Andriana
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.v6i2.3825

Abstract

The business sector faced intensifying competition due to significant advancements in information systems and technology. PT. Florindo Makmur, a leading private company in the cassava processing industry producing tapioca flour, has proven to implement quality standards to uphold product quality and ensure customer satisfaction. The product quality inspection process had to meet standards before packaging; however, reporting remained manual using paper sheets, elevating the risk of data loss and reducing monthly evaluation efficiency due to manual calculations. The aim of this research was to design an efficient information system for monitoring product quality at PT. Florindo Makmur, utilizing the Statistical Quality Control (SQC) method. The quality control monitoring system played a central role in gathering quality control data to support management decisions regarding product quality certainty. Therefore, obtaining monitoring information promptly was crucial to ensure products met quality standards and reduce rejected product quantities. The research approach included observation, interviews, and literature review as data collection strategies, while the system development method used was the waterfall method encompassing system requirement analysis, design, coding, and implementation. This information system enabled PT. Florindo Makmur to efficiently monitor its products by applying SQC concepts such as data analysis and creating control charts to swiftly identify improvements in product defects and take appropriate actions.

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