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All Journal JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science Science in Information Technology Letters Applied Engineering and Technology Bulletin of Social Informatics Theory and Application
Leonel Hernandez, Leonel
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : Knowledge Engineering and Data Science

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Docker Optimization of an Automotive Sector Virtual Server Infrastructure Hernandez, Leonel; Rios, Carlos Eduardo Uc
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p71-85

Abstract

Server virtualization is a powerful strategy for optimizing network infrastructure. It allows multiple virtual servers to run on a single physical server, maximizing resource utilization and improving efficiency. Deploying server virtualization using Docker technology offers a lightweight and flexible approach to optimizing network infrastructure. Docker contains package applications and their dependencies, enabling consistent and efficient deployment across various environments. Specifically, optimizing virtual server infrastructure using Docker Technology in the automotive sector focuses on improving the efficiency and management of the company's virtual server resources. By implementing Docker technology, a container platform that allows the packaging and running of applications in a lightweight and secure manner, the project aims to reduce operational costs and increase the agility and scalability of IT services. Adopting Docker will facilitate the rapid deployment of applications, ensuring a consistent and isolated execution environment for each one. This will allow the company to manage its workloads more efficiently and respond quickly to market needs, reassuring the audience about the potential improvements in their work processes. The study is developed under the top-down methodology guidelines for the design of telematics systems. It also includes a detailed analysis of the current server performance, a proposal for restructuring the existing infrastructure, and a plan to implement DevOps practices to optimize development and operational processes. With these changes, a significant improvement in system availability and performance is expected, thus contributing to the company's growth and technological innovation. The benefits of Docker implementation are numerous, including lightweight (containers share the host OS kernel, reducing overhead), portability (consistent environment across development, testing, and production), scalability (effortlessly scale containers horizontally), isolation (each container runs in its isolated environment), and efficiency (optimal resource utilization compared to traditional VMs). These benefits promise a brighter future for the company's IT infrastructure.
Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Akbari, Ade Kurnia Ganesh; Fadhilla, Akhmad Fanny; Triono, Alfiansyah Putra Pertama; Paramarta, Andien Khansa’a Iffat; Setyaputri, Faradini Usha; Hernandez, Leonel
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p170-187

Abstract

Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system.
Co-Authors A.N. Afandi Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Albas, Juan Andri Pranolo Anik Nur Handayani Anusua Ghosh, Anusua Camargo, Jair Cantillo, Andres Carbono, Jair Dedes, Khen Erna Daniati Fachrul Kurniawan Fadhilla, Akhmad Fanny Fajarwati, Erliana Hoz, César De La Marchena, Piedad Paramarta, Andien Khansa’a Iffat Putra Utama, Agung Bella Rios, Carlos Eduardo Uc Setyaputri, Faradini Usha Triono, Alfiansyah Putra Pertama Utama, Agung Bella Putra Wahyu Sakti Gunawan Irianto