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Andri Pranolo
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Office 1 ASCEE Secretariat RUMAH KOTAK Jl. Kranginan, Mertosanan Kulon, Potorono, Kec. Banguntapan, Kabupaten Bantul, Daerah Istimewa Yogyakarta 55196, Indonesia Office 2 ASCEE Secretariat Jl. Raya Janti No.130B, Karang Janbe, Karangjambe, Kec. Banguntapan, Kabupaten Bantul, Daerah Istimewa Yogyakarta 55198, Indonesia
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Daerah istimewa yogyakarta
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Applied Engineering and Technology
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 28294998     DOI : http://dx.doi.org/10.31763/aet
Core Subject : Science, Engineering,
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology
Applied Engineering and Technology provides a forum for information on innovation, research, development, and demonstration in the areas of Engineering and Technology applied to improve the optimization operation of engineering and technology for human life and industries. The journal publishes original papers, review articles, technical notes, and letters to the editor. Authors are encouraged to submit manuscripts that bridge the gaps between research, development, and implementation. The breadth of coverage ranges from innovative technologies and systems of implementation and application development to better human life and industry. The following scope are welcome: Aerospace Engineering, Automobile Engineering, Applied Mathematics, Applied Physics, Bioinformatics, Biophysics, Biotechnology, Chemical Engineering, Chemical Physics, Civil Engineering, Computational Physics, Computer Engineering, Electrical Engineering, Electronic Engineering, Energy Engineering, Environment Engineering, Information Technology, Marine engineering, Mechanical engineering, Medical Engineering, Medical imaging, Medical Physics, Nanotechnology, Ocean Engineering, Optical engineering, Photonics, Robotics, Urban Engineering and Other related engineering topics in general.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 4 Documents
 1 
Search results for , issue "Vol 3, No 3 (2024): December 2024" : 4 Documents clear
A compact patch antenna for wireless sensor network applications in WLAN Ahmed, Md. Firoz; Bashir, Samiul; Paul, Pronab Kumar; Islam, Md. Bipul; Uddin, A.N.M. Shihab; Kabir, M. Hasnat
Applied Engineering and Technology Vol 3, No 3 (2024): December 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i3.1702

Abstract

In wireless sensor networks (WSNs), antennas play a crucial role in enlarging network capacity, prolonging transmission distances, fostering spatial reuse, and minimizing interference. This paper delineates a miniature rectangular patch antenna featuring partial grounding, meticulously engineered for the WLAN (wireless local area network) to promote real-time operations within WSNs. The main goal is to augment the creation and execution of a patch antenna that aligns with the typical size and power constraints of WSN nodes. The antenna is engineered and simulated for a 2.4 GHz WLAN frequency band (2.4 – 2.48 GHz) by leveraging CST Microwave Studio 2024. It is fabricated on a 45 mm × 50 mm FR4 substrate (εr = 4.3, thickness = 1.4 mm, loss tangent = 0.025). The antenna is energized via a 50 Ω microstrip inset-feed line. This antenna demonstrates a substantial bandwidth of 159.729 MHz (2.31963 GHz to 2.479359 GHz), an impressive return loss of – 48.15956 dB, a VSWR (voltage standing wave ratio) of 1.007848, a directivity of 4.7 dBi, a gain of 3.04 dBi, and an efficiency of 68.21%. These performance indicators illustrate the antenna’s effectiveness in enabling short-range communication within WSNs. With its compact design, broad bandwidth, and strong performance metrics, this antenna is an efficient and cost-effective solution suitable for various applications in WSNs, including industrial automation, environmental monitoring, healthcare, and smart city initiatives, ensuring reliable and high-quality wireless communication.
Sustainable urban development: a case study on green infrastructure implementation in Kota City India Lal, Shiv; Choudhary, Saaransh; Kakodia, Ashok Kumar
Applied Engineering and Technology Vol 3, No 3 (2024): December 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i3.1741

Abstract

Kota City is known as an educational city with increasing urbanization, requiring a sustainable development approach to address environmental and social challenges. One of the solutions implemented is green infrastructure, which integrates natural elements to improve ecological quality, reduce environmental pressure, and improve people's welfare. This study aims to evaluate the effectiveness of green infrastructure in supporting sustainable development in Kota City. The approaches studied include rainwater management, renewable energy (solar, wind, nuclear, hydro), sustainable transportation, and red light-free zone policies to reduce energy consumption and pollution. The study results show that implementing green infrastructure significantly lowers urban temperatures, improves flood management, improves air quality, and improves energy efficiency. These approaches can help for sustainable urban development. This research provides benefits in the form of a greener, more efficient, and sustainable urban development model. With this approach, Kota city can be an example for other cities in creating a healthier, environmentally friendly environment and improving the economic and social welfare of the community.
Analysis of horizontal milling machine vibration on the influence of gear module cutters with sizes M1 and M1.5 Zaira, Jupri Yanda; Haiqal, Muhammad; Sitinjak, Bherry Arif; Prasetyo, Yoga Ali; Hasibuan, Muhammad Refky; Alhabib, Fauzan; Sinaga, Indra; Hardyanto, Rio
Applied Engineering and Technology Vol 3, No 3 (2024): December 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i3.1762

Abstract

This study examines the effect of vibrations on the horizontal milling machine type 1216 during gear manufacturing using cutter modules with diameters of 50 mm and 55.25 mm, each at a cutting depth of 1 mm. Displacement, velocity, and acceleration measurements were conducted in vertical, horizontal, and axial directions using a VM-6370 vibration meter, with the average vibration amplitudes analyzed. The results revealed that the 55.25 mm cutter produced the highest vibration amplitude in the horizontal direction, reaching 353.270 mm/s², while the lowest was in the vertical direction at 171.293 mm/s². For the 50 mm cutter, the highest amplitude occurred in the vertical direction at 0.1336 mm and the lowest in the horizontal direction at 0.0583 mm. These findings demonstrate that larger cutter modules generate higher vibration amplitudes, significantly affecting the precision and surface quality of gear manufacturing. The study emphasizes the importance of selecting appropriate cutter sizes to minimize vibrations, optimize manufacturing processes, and improve product quality. By providing a detailed analysis of the relationship between cutter size and vibration levels, this research is a valuable reference for enhancing the efficiency and accuracy of gear cutting in industrial applications.
Ensemble learning approaches for predicting heart failure outcomes: A comparative analysis of feedforward neural networks, random forest, and XGBoost Ariyanta, Nadindra Dwi; Handayani, Anik Nur; Ardiansah, Jevri Tri; Arai, Kohei
Applied Engineering and Technology Vol 3, No 3 (2024): December 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i3.1750

Abstract

Heart failure is a leading cause of morbidity and mortality worldwide, and early prediction of outcomes is critical for timely intervention and improved patient care. Accurate prediction models can help clinicians identify high-risk patients, optimize treatment strategies, and reduce healthcare costs. In this study, we developed and evaluated machine learning models to predict mortality in patients with heart failure using a medical dataset of 299 patients with 13 clinical variables collected in 2015. Four models were tested, including a Feedforward Neural Network (FNN), Random Forest, XGBoost, and an ensemble model combining all three models. The experimental process included data preprocessing, feature scaling, and stratified cross-validation to ensure robust evaluation. The results showed that the ensemble model achieved the best performance with an ROC-AUC of 0.9134 and an F1 score of 0.7439, outperforming individual models such as Random Forest (ROC-AUC: 0.9117) and XGBoost (ROC-AUC: 0.9130). FNN, despite having the highest accuracy (0.8455), showed lower performance in terms of recall and precision, likely due to its sensitivity to overfitting on small datasets. These results highlight the effectiveness of ensemble learning in medical prediction tasks, especially for handling complex, high-dimensional health data. The proposed ensemble model has the potential to be integrated into clinical decision support systems, enabling real-time risk assessment and personalized treatment plans for heart failure patients. Future research should explore larger, multicenter datasets, incorporate advanced feature engineering techniques, and investigate the integration of deep learning architectures such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to process sequential data such as ECG signals.

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