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INDONESIA
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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Improving Information Security with Machine Learning Ahmad Sanmorino; Rendra Gustriansyah; Shinta Puspasari; Juhaini Alie
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.v6i1.3317

Abstract

The study Improving Information Security with Machine Learning explores the fusion of machine learning methodologies within information security, aiming to fortify conventional protocols against evolving cyber threats. By conducting a comprehensive literature review and empirical analysis, this scholarly endeavor highlights the efficacy of machine learning in anomaly detection, threat identification, and predictive analytics within security frameworks. Through practical demonstrations, such as z-score-based anomaly detection in network traffic data and NLP-based email security systems, the study illustrates the practical applications of machine learning techniques. Additionally, it delves into the mathematical underpinnings of predictive analytics and the architecture of neural networks for malware detection. However, while showcasing the transformative potential of machine learning, the study also confronts significant challenges. Ethical, legal, and privacy considerations emerge prominently, emphasizing the need for regulations addressing algorithmic biases, ethical dilemmas, and data protection. Moreover, the study emphasizes the practical challenges of scalability, interpretability, continual adaptation to evolving threats, and the harmonious interaction between human expertise and machine intelligence. By offering practical recommendations and future research directions, this scholarly exploration aims to empower researchers, practitioners, and policymakers in navigating the complex intersection of machine learning and information security, thereby fostering innovation and comprehension in this evolving domain.
Analysis of Base Station Replacement Using the Cost-231 Propagation Model and Stanford University Interim (SUI) on the LTE Network in Pauh District, Padang Afrizal Yuhanef; Zurnawita; Nadia Pratiwi; Herry Setiawan
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.v6i1.3331

Abstract

The increase in network traffic is caused by an imbalance between the number of users and the provision of network capacity, causing the decrease of network performance. This is often found in densely populated areas with a large number of users so that the use of cellular access services is fairly high. For this reason, it is necessary to optimize the network, one of which is by replacing existing devices (swap). Site 31140_Lubuk Bukit, Pauh District, Padang is one of the base stations that has a performance issue, which has a low throughput value (±5Mbps) and a high traffic value of 83 GB/day with Flexi Multi Radio device type and sectoral antenna. Therefore, the existing equipment is replaced, from the FMR type to the Airscale type which minimizes the use of sectoral equipment and antenna types (3 sectors) to multisectoral (6 sectors). The measurement process is carried out using two methods, namely the COST-231 propagation model for the frequency of 1800 MHz and the Stanford University Interim (SUI) for the frequency of 2100 MHz then the Key Performance Indicator (KPI) parameters with the parameters of accessibility, retainability, integrity, mobility, usage, propagation loss and receive power. The results showed that the optimization carried out by the swap method could improve the site's performance, namely a decrease in traffic reaching 31.42% and an increase in throughput up to 60% and followed by an increase in other parameters that have met the threshold value.
Using Genetic Algorithm to Solve Puzzle Games: A Review Iksan Bukhori; Jason Felix; Saddam 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.v6i1.3348

Abstract

Puzzles have been recognized for their development as a popular form of entertainment due to their ability to intricately challenge the mind while engendering creativity in the player. The development of puzzle games has given rise to a new generation of puzzle games characterized by diverse sequences and different image variations. With the rapid development of puzzle games, we looked at solving approaches using Genetic Algorithms (GA). In this paper, we try to analyze several puzzle games such as Sliding Blocks, Sudoku, Tic-Tac-Toe, and Jigsaw that can be solved using GA. We found that 120 papers have examined the use of GA for puzzle games, and eliminated into 14 papers. We evaluated these 14 papers for each puzzle game we selected by comparing the chromosome representation, GA operator, GA parameters, and the results. Based on the discussion, the application of GA to solve puzzle games can be effectively executed with a high degree of accuracy. Puzzle games that use measurement methods such as Sliding Block, Sudoku, and Jigsaw run in a similar pattern. What is common to all of them is that the chromosomes are represented as matrices or arrays in all cases, and standard genetic operators such as selection, crossover, and mutation are used. The population size is large, often 1000 chromosomes, and parameters such as mutation rate are kept low, around 5%. On the other hand, the performance of GA for solving Tetris and Tic-Tac-Toe from each publication cannot be compared due to different measurement methods and metrics.
E-Commerce Application with Web Engineering Method Website Based Nurhabibah Febrianty Hasibuan; Raissa Amanda Putri; Aninda Muliani Harahap
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.v6i1.3353

Abstract

CV Jon Indo merupakan perusahaan yang bergerak di bidang material pabrik sebagai supplier, kontraktor, dan stockist yang memperdagangkan material pabrik lainnya. Untuk bersaing secara global, salah satu alat atau tools yang dapat menjangkau pasar yang diharapkan dalam hal ini adalah peningkatan penjualan produk. CV. Jon Indo belum memiliki sistem pemesanan yang mampu menjangkau banyak konsumen yaitu masih menjual barangnya melalui WhatsApp dan merekapitulasi data penjualan secara manual sehingga terdapat permasalahan seperti kesalahan penjadwalan pengiriman barang, serta kesalahan input produk. pesanan sehingga menyebabkan penjualan menurun. Penelitian ini bertujuan untuk merancang sebuah aplikasi yang ditampilkan pada CV Jon Indo untuk meningkatkan volume penjualan dan memperluas pemasaran produk CV Jon Indo. Sistem yang dibangun mempunyai fitur promosi dan penambahan CTA (call to action) pada website, kemudian pada sistem pengiriman barang peneliti membuat fitur tracking dan fitur permintaan barang sesuai keinginan pelanggan. Aplikasi berbasis web ini dirancang dengan menggunakan metode Research and Development (RnD) dan Metode Web Engginering sebagai perancangan aplikasi E-Commarece. Bahasa pemrograman yang digunakan untuk implementasi E-comarece menggunakan bahasa pemrograman PHP dan database MySql. Aplikasi yang dibuat dapat digunakan untuk memudahkan penjualan, pembelian dan penyebaran informasi produk terbaru.
Building Digital Platform for Property Marketing Sales with an Enterprise Architecture Approach Djarot Hindarto; Tri Dharma Putra
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.v6i1.3361

Abstract

Constructing a digital platform is an essential priority in an era where technology is the cornerstone of success in the real estate marketing and sales sector. Nevertheless, the advancement of these platforms is frequently impeded by obstacles pertaining to integration, security, and scalability, which stem from their inadequate establishment. Existing platforms' incapability to rapidly adapt to shifting market dynamics frequently impedes the development of innovative digital solutions for the real estate industry, which provided the impetus for this study. The fundamental objective of this study is to create a framework capable of accommodating scalable business expansion, enhancing data security, and overcoming integration obstacles. Utilizing Enterprise Architecture principles in the design and implementation of the platform, as well as conducting a comprehensive examination of the current IT infrastructure, stakeholder requirements, and mapping of pertinent business processes, will comprise the research methods. The results of this study are expected to contribute to a wholehearted comprehension of how the Enterprise Architecture approach can function as a resilient framework for the development of effective digital platforms. In the contemporary digital age, this platform is expected to furnish solutions capable of swiftly adjusting and reacting to evolving market dynamics and changes, thereby facilitating the marketing and sales requirements of real estate. Finally, this research endeavors to offer a comprehensive and practical perspective on constructing a robust and flexible digital infrastructure that can effectively cater to the demands of the real estate sector in the current era of digitalization.
a Comparative Study of Iconnet Jabodetabek and Banten Using Linear Regression and Support Vector Regression Muhaimin Hasanudin; Ifan Prihandi; Sifania Nazua
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.v6i1.3362

Abstract

PT PLN Indonesia Comnets Plus has little information regarding future customer growth, making it difficult to take steps to meet customer needs. This research aims to predict customer growth in the future based on quantitative data from the previous year, where the output provided produces data in the form of numbers that are analyzed using statistical methods. The hope is to provide information to maximize customer growth with the minimum area or bandwidth used by the company. This research uses linear regression and support vector regression (SVR) algorithms using a company secondary dataset of 252 data points with 5 attributes. Data was collected during the last one-year period, from January to December 2021. The results of the research show that predictions using both algorithms have increased, customer growth when viewed from the number of customers, bandwidth, and regional data has increased significantly. This can be seen from the value of the number of customers, which continues to increase, while the highest number of customers falls in December, the most requested bandwidth is 20 mbps, and the largest customer area is in the Depok area. The results of the research show that the SVR algorithm is superior in terms of mean absolute percentage error (MAPE): 0.02% MAPE, 0.10 MAE, and 0.99 RMSE, while for linear regression, the MAPE values were 36.28%, MAE 201, and RMSE 0.80.
Enterprise Architecture Design and Implementation for IoT Integration in Manufacturing Electrical Panels Djarot Hindarto; Ferial Hendrata; Mohammad Iwan Wahyuddin; Sigit Wijanarko
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.v6i1.3365

Abstract

Internet of Things technology has transformed manufacturing efficiency and optimization. Electrical panel manufacturing benefits from Internet of Things for better functionality, predictive maintenance, and smoother operations. This study examines the design and implementation of an Enterprise Architecture strategy for seamless Internet of Things integration in electrical panel manufacturing. This research aims to explain Enterprise Architecture and use it as a framework for Internet of Things integration in electrical panel manufacturing. This study examines the complex relationships between Internet of Things components, their connectivity, and a broad Enterprise Architecture framework needed to organize their functionality. This integration uses Enterprise Architecture principles to optimize resource use, reduce downtime, and improve manufacturing efficiency. This effort involves analyzing existing infrastructure, identifying Internet of Things deployment points, and creating an Enterprise Architecture plan that meets business goals. This research emphasizes the need for close IT-operations collaboration to achieve a unified vision and smooth Internet of Things integration. This research addresses Internet of Things implementation challenges in manufacturing, including security, data interoperability, and scalability. Strong governance and adaptable architecture are stressed to address these challenges within an Enterprise Architecture framework. This research aims to help electrical panel manufacturers harness the transformative power of the Internet of Things. Strategic Enterprise Architecture helps businesses navigate complexity, leverage Internet of Things, and create a more agile, connected, and optimized manufacturing landscape.
Enhancing Business: Incorporating Enterprise Architecture into Project Management in the Food Manufacturing Industry Djarot Hindarto; Tri Dharma Putra; Mohammad Iwan Wahyuddin
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.v6i1.3368

Abstract

The present research examines the incorporation of Enterprise Architecture into project management in the food manufacturing sector with the aim of enhancing operational efficiency and corporate accountability. This study investigates the use of an integrated Enterprise Architecture strategy with project management in the food industry to improve production processes by using the crucial role of information technology. The aim of this approach is to enhance operational frameworks, customize information systems, and guarantee the congruence between business strategic aims and technology implementation. Within this framework, the analysis centers on the potential of integrating Enterprise Architecture to enhance transparency, interoperability, and scalability in the food manufacturing industry. Enterprise Architecture offers a comprehensive perspective on the technological infrastructure needed to ease effective and adaptable business operations. The implementation of Enterprise Architecture yields advantages in elucidating system architecture, enhancing coordination among diverse business components, and easing the more adaptable assimilation of modifications. Enterprise Architecture is crucial in project management as it eases improved decision-making and more efficient risk management. This study emphasizes the significance of incorporating Enterprise Architecture into the management of projects in the food industry as a strategic basis for ongoing operational advancement and enhancement while simultaneously prioritizing product quality, production efficiency, and responsiveness to evolving market demands.
Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study 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.v6i1.3371

Abstract

The current research aims to assess the precision of forest fire detection using CNN and VGG16 models, specifically in the context of fire identification. While both models have demonstrated significant promise in visual pattern recognition, a comprehensive analysis regarding their specific benefits in forest fire identification is still needed. The rationale behind this research stems from the significance of promptly identifying forest fires as a preemptive measure to mitigate their detrimental effects on the environment and society. The employed approach involves the application of transfer learning techniques on a diverse and extensive dataset encompassing different forest fire scenarios. The dataset was used to train both CNN and VGG16 models. The test results indicated that the CNN model achieved a forest fire detection accuracy of 96%, while VGG16 achieved 98% accuracy. The primary objective of this research is to enhance comprehension regarding the merits and demerits of each model in the context of forest fire identification scenarios. While VGG16 exhibits marginally superior performance in identifying forest fires, this discrepancy offers valuable insight into the practical applicability of these two models for fire detection in real-world scenarios. These findings establish a solid basis for the advancement of more dependable and efficient early detection technology in the prevention and management of forest fires in the future. This can be accomplished by capitalizing on the unique capabilities of each model to optimize their performance in practical scenarios.
Toddlers’ Nutritional Status Prediction Using the Multinomial Logistics Regression Method Rendra Gustriansyah; Nazori Suhandi; Shinta Puspasari; Ahmad Sanmorino; Dewi Sartika
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.v6i1.3372

Abstract

Malnutrition is one of the foremost health problems experienced by children under five in many countries, especially in low and middle-income countries. Meanwhile, the target of Sustainable Development Goals (SDGs) 2.2 is that all forms of malnutrition must end by 2025. Therefore, this study aims to predict the toddlers’ nutritional status (malnutrition, undernutrition, overnutrition, and normal nutrition) based on age, body mass index (BMI), weight, and length using the Multinomial Logistic Regression (MLR) classification method. The dataset consists of two hundred toddlers obtained from the Kaggle site. Following pre-processing, the dataset is divided, with 80 percent of the data for training and the remaining 20 percent for testing. The model was trained using 10-fold cross-validation (CV). In Addition, the MLR model performance was evaluated using the confusion matrix (CM), the area under the curve (AUC), and the Kappa coefficient (KC). The evaluation results using CM show that the accuracy, sensitivity, and specificity values are 0.9412, 0.9375, and 0.9790, respectively. AUC and KC also show excellent results. It indicates that the MLR method is an esteemed and recommended method for predicting the nutritional status of toddlers. Therefore, this research can contribute to providing early information so that the Government can immediately determine the necessary treatment.

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