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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
 1 
Search results for , issue "Vol. 1 No. 02 (2022): Journal of Novel Engineering Science and Technology" : 5 Documents clear
CFD Analysis of the Eccentricity Ratio on Journal Bearing due to Differences in Lubrication Type Vivi Failawati; Mohamad Yamin; Sri Poernomosari; Amar Ratnakar Naik
Journal of Novel Engineering Science and Technology Vol. 1 No. 02 (2022): 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 | Full PDF (762.303 KB) | DOI: 10.56741/jnest.v1i02.111

Abstract

Lubrication on journal bearings plays a vital role in reducing metal-to-metal friction, separates the reliable components. Often there is a decrease in lubrication conditions, which will cause failure or a change in roughness to the inner surface. Direct metal-to-metal contact between the bearing's inner and journal surface will gradually deform the bearing. In diesel engine applications, lubrication is aiming to minimize failure or damage to the journal bearing. This paper investigates a 3D CAD model of a journal bearing using the ANSYS Computational Fluid Dynamics software. The effect of eccentricity ratio is analyzed for semisynthetic lubricating oil R3 10W-30 and synthetic oil. The result shows that synthetic oil has a considerable pressure compared to R3 10W-30, especially at a higher eccentricity ratio.
Safety Stock, Warehouse Capacity, and Return of Goods in Inventory Model Development Roland Y. H. Silitonga; Valyn Alanda Saputra; Aamir Khan
Journal of Novel Engineering Science and Technology Vol. 1 No. 02 (2022): 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 | Full PDF (545.482 KB) | DOI: 10.56741/jnest.v1i02.120

Abstract

With the increasing level of competition between companies, companies are required to be able to manage their inventory system optimally. In the pharmacy supply system, it is important to consider the uncertainty factor because the demand for medicines depends on the uncertain nature of the disease, besides that there is an expiration factor that must be considered because medicines must have an expiration date. To solve the problem of expiration, the supplier usually provides a return policy in accordance with the specified conditions. In addition, pharmacies also have limitations on warehouse capacity that must be considered. To solve the problem of limited warehouse capacity in this study, an approach to the requirements of the Karush Kuhn-Tucker method was used. From the developed model, two ordering times are obtained, namely and which will be compared to get the optimal ordering time. From the analysis of the model, it is found that the more characteristic considerations in an inventory system, the greater the total inventory cost.
Rapid Preliminary Sailplane Design using Regression Method Mohamad Yamin; Ahmad Fakhri Giyats
Journal of Novel Engineering Science and Technology Vol. 1 No. 02 (2022): 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 | Full PDF (1192.121 KB) | DOI: 10.56741/jnest.v1i02.143

Abstract

The article describes a new method for the preliminary design of a Sailplane based on technical data from similar Sailplanes, enabling quick design using regression methods. The authors chose FX 66-168 as the main wing airfoil for a new sailplane with a gross weight of 602 kg with a flight parameter Angle-of-Attack (AoA) 00 in minimum velocity of 49.5 m/s to cruise. As an example, we illustrate the proposed regression method. Our method allows for improving the effective preliminary design of the sailplane. The effectiveness evaluation validation of the new model is conducted with the numerical calculation using XFLR Opensource software. The research results improve design techniques and could be used as a manual for an aircraft designer.
Deep Reinforcement Learning for Tehran Stock Trading Neda Yousefi
Journal of Novel Engineering Science and Technology Vol. 1 No. 02 (2022): 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 | Full PDF (673.52 KB) | DOI: 10.56741/jnest.v1i02.171

Abstract

One of the most interesting topics for research, as well as for making a profit, is stock trading. It is known that artificial intelligence has had a great influence on this path. A lot of research has been done to investigate the application of machine learning and deep learning methods in stock trading. Despite the large amount of research done in the field of prediction and automation trading, stock trading as a deep reinforcement-learning problem remains an open research area. The progress of reinforcement learning, as well as the intrinsic properties of reinforcement learning, make it a suitable method for market trading in theory. In this paper, single stock trading models are presented based on the fine-tuned state-of-the-art deep reinforcement learning algorithms (Deep Deterministic Policy Gradient (DDPG) and Advantage Actor Critic (A2C)). These algorithms are able to interact with the trading market and capture the financial market dynamics. The proposed models are compared, evaluated, and verified on historical stock trading data. Annualized return and Sharpe ratio have been used to evaluate the performance of proposed models. The results show that the agent designed based on both algorithms is able to make intelligent decisions on historical data. The DDPG strategy performs better than the A2C and achieves better results in terms of convergence, stability, and evaluation criteria.
Telemedicine and COVID-19 Pandemic: Valuable Lessons for Future Implementations Urfa Khairatun Hisan; Irianto Irianto; Ihwan Ghazali; Muhammad Miftahul Amri
Journal of Novel Engineering Science and Technology Vol. 1 No. 02 (2022): 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 | Full PDF (627.116 KB) | DOI: 10.56741/jnest.v1i02.237

Abstract

Telemedicine has gained significant momentum during the pandemic. Telemedicine offers a way for people to access medical care without having to physically visit a healthcare facility, which can be especially important when in-person visits may not be safe due to a highly contagious virus. One major benefit of telemedicine is that it allows for the continuation of healthcare services while also helping to reduce the spread of COVID-19. By providing virtual consultations and treatments, telemedicine helps to limit the number of in-person visits. This is critical to reduce the risk of exposure to the virus and prevent healthcare facilities from becoming overwhelmed. Telemedicine has also helped to address some challenges that have arisen as a result of the pandemic. For instance, by using telemedicine, healthcare providers can reduce the need for PPE and maintain a safe distance while still providing necessary medical care. Despite the many benefits, there are also some challenges and limitations. For example, not everyone has access to the necessary resource to participate in telemedicine. Additionally, some medical procedures and treatments may not be suitable for telemedicine, requiring in-person visits. Moreover, it is important to consider the ethical issues that it raises and to work toward solutions that address these concerns. This can involve measures such as ensuring that telemedicine is available and accessible to all who need it, protecting patient privacy and confidentiality, and ensuring that telemedicine is used in a way that provides the highest quality and most appropriate care for patients.

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