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Journal of Novel Engineering Science and Technology
Published by Indonesian Institute of Science and Technology Research
ISSN : 29618916     EISSN : 29618738     DOI : https://doi.org/10.56741/jnest.v1i02
Core Subject : Science, Social, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science Mechanical Engineering
Journal of Novel Engineering Science and Technology is a multi-disciplinary international open-access journal dedicated to natural science, technology, and engineering, as well as its derived applications in various fields. JNEST publishes high-quality original research articles and reviews in all of the disciplines mentioned above. All papers submitted will go through a rapid peer-review process to ensure their quality. Submissions must contain original research and contributions to their field. The manuscript must adhere to the author’s guidelines and have never been published before. All accepted manuscripts will be indexed in DOAJ, EBSCO, and Google Scholar. The indexation in SINTA, Scopus, and WoS will be provided in the future to provide maximum exposure to the articles.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 2 No. 01 (2023): Journal of Novel Engineering Science and Technology" : 5 Documents clear
Latest Trends in Visual Manipulation and Navigation in Robotics Miftahul Amri, Muhammad; Areche, Franklin Ore; Ratnakar Naik, Amar
Journal of Novel Engineering Science and Technology Vol. 2 No. 01 (2023): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v2i01.253

Abstract

In recent decades, the term Robot has become more and more popular. A robot can be defined as a machine that is specifically built to complete certain tasks to help human-being. In order to successfully accomplish its task, the robot needs to receive input data and process it. Then, the processed data is used for manipulator-actions decision-making. The input data can vary from sound, temperature, vibration, touch, vision, etc. Among those input data, vision is arguably one of the most challenging data. This is because vision often needs detailed and complicated preprocessing before it can be used. In addition, vision data size is relatively larger compared to the other type of input data, making it more challenging to process considering the computational resources. In this paper, current research and future development trend of robotic vision were reviewed and discussed. Further, challenges and potential issues about robot vision, such as safety and privacy concerns, were also discussed.
The Application of the Technology Readiness Acceptance Model on Education Nafia, Zidnii Ilma; Hidayati, Dian; Sulisworo, Dwi
Journal of Novel Engineering Science and Technology Vol. 2 No. 01 (2023): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v2i01.265

Abstract

The development of science and technology has changed the work system, including in educational institutions. Rapid information and communication technology changes are now essential factors in changing education management. Improving the quality of education can leverage the quality of a nation. Technology in the teaching field provides many opportunities in various aspects of education and learning, such as academic services, access to learning materials, educational evaluation, management of lesson content, reports on student learning outcomes and the use of digital-based learning media by teachers. The use of technology in learning can create a competitive environment for students and teachers to be more creative and innovative. Education in Technology Study (Edtech) has become a global concern, especially during a pandemic. The revitalization and development of teacher and student synergy in implementing digital technology-based education will accelerate the education revolution 4.0.
IoT-based Single-Phase Power Factor and Control Panel Monitoring System Subrata, Arsyad Cahya; Perdana, Muhammad Sukmadika; Ariyansyah, Qolil
Journal of Novel Engineering Science and Technology Vol. 2 No. 01 (2023): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v2i01.270

Abstract

The power factor is a value obtained by comparing the actual power value and apparent power in an electric circuit. Because it is related to the quality of the distributed power, this power factor needs to be monitored. Devices with inductive loads generally cause power factor distortion, causing losses. Power factor monitoring is carried out periodically to ensure the efficiency of electricity distribution to the building. Power factor monitoring is usually done on the control panel of a building by measuring the voltage and current flowing. Manual monitoring could be more ineffective in terms of time and effort and has the potential for recording errors. This study proposes a power factor monitoring system on the control panel to facilitate recording. The system created is integrated with IoT technology so that it can monitor and record automatically anywhere and anytime. The developed system has an error percentage of 1.53% for the voltage sensor and 5.02% for the current sensor.
Determining the Particle Size of Cu and Ni in Thin Cu/Ni Films using the Williamson-Hall Method Rahmatika, Zulfa ‘Amalia; Toifur, Moh.
Journal of Novel Engineering Science and Technology Vol. 2 No. 01 (2023): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v2i01.311

Abstract

This research focuses on analyzing the particle size of a thin Cu/Ni layer produced through electroplating by varying the input voltage. The Williamson-Hall method is used to determine the particle size of the layer, and an X-ray diffractometer is used to characterize the layer. The study finds that the particle size of Cu and Ni layers with different applied voltages has different values due to various factors. The optimum voltage for the Ni layer is found to be 7.5 volts, and its overall particle size is 4.13 × 10^(-10) nm, while the particle size of Cu is 5.00 × 10^(-9) nm. The applied voltage affects the particle size produced, and the research identifies an optimum voltage at 7.5 volts.
AI Big Data System to Predict Air Quality for Environmental Toxicology Monitoring Jufriansah, Adi; Khusnani, Azmi; Pramudya, Yudhiakto; Sya’bania, Nursina; Leto, Kristina Theresia; Hikmatiar, Hamzarudin; Saputra, Sabarudin
Journal of Novel Engineering Science and Technology Vol. 2 No. 01 (2023): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v2i01.314

Abstract

Pollutants in the air have a detrimental effect on both human existence and the environment. Because it is closely linked to climate change and the effects of global warming, research on air quality is currently receiving attention from a variety of disciplines. The science of forecasting air quality has evolved over time, and the actions of different gases (hazardous elements) and other components directly affect the health of the ecosystem. This study aims to present the development of a prediction system based on artificial intelligence models using a database of air quality sensors.This study develops a prediction model using machine learning (ML) and a Decision Tree (DT) algorithm that can enable decision harmonization across different industries with high accuracy. Based on pollutant levels and the classification outcomes from each cluster's analysis, statistical forecasting findings with a model accuracy of 0.95 have been achieved. This may act as a guiding factor in the development of air quality policies that address global consequences, international rescue efforts, and the preservation of the gap in air quality index standardization.

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