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All Journal Emerging Science Journal
Pramudita, Aloysius A.
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Environmental Science

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NOMA Performance Improvement with Downlink Sectorization Vidyaningtyas, Hurianti; Iskandar, .; Hendrawan, .; Pramudita, Aloysius A.
Emerging Science Journal Vol 9, No 1 (2025): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-01-017

Abstract

This study tackles the growing challenge of inter-user interference in Non-Orthogonal Multiple Access (NOMA) systems, particularly as user density increases in modern communication networks. The primary objective is to improve system performance by implementing a downlink sectorization strategy, which groups users into distinct sectors to manage interference and optimize resource allocation. A Sequential Power Allocation (SePA) algorithm was introduced to enhance power distribution within sectors, aiming to maximize both user capacity and overall sum rate. The methods employed included detailed simulations comparing the performance of traditional NOMA systems and those incorporating sectorization. The results demonstrate that sectorization can significantly boost the system’s sum rate by up to 25% and reduce decoding errors by as much as 51%, particularly when the number of users per sector is kept under 20. However, performance saturation occurs beyond this threshold, where additional users do not contribute to further improvements. The novelty of this research lies in applying spatial sectorization to NOMA, showing that spatial sectorization can minimize intra-sector interference, improve power efficiency, and maintain reliable communication in high-demand environments such as the Internet of Things (IoT). This study provides valuable insights for optimizing NOMA systems, crucial for next-generation wireless networks. Doi: 10.28991/ESJ-2025-09-01-017 Full Text: PDF
Empoasca Pest Attack Classification on Tea Plantations Using Multispectral Imaging and Deep Learning Kurniawan, Bella K.; Suratman, Fiky Y.; Fauziah, Fani; Vidyaningtyas, Hurianti; Saputri, Desti M.; Pramudita, Aloysius A.
Emerging Science Journal Vol. 9 No. 4 (2025): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-04-04

Abstract

This study aims to enhance the management of Empoasca pests in tea cultivation, a critical sector for Indonesia’s economy, by developing an innovative detection method. The challenge of pest infestations may significantly reduce tea production yields, and the misuse of chemical pesticides further compromises tea quality. We propose a novel approach that integrates multispectral imaging with Convolutional Neural Networks (CNN), specifically employing ResNet-50 and AlexNet architectures to accurately detect Empoasca infestations. We begin with the data collection process, followed by the development of the preprocessing model and evaluation of its performance. We classify tea leaves affected by Empoasca pests using spectral data obtained from a multispectral camera operating across Green, NIR (Near Infrared), REG (Red Edge), and RED channels. We evaluated various spectral channels and identified the green spectrum as the most effective for revealing visual characteristics, such as curled leaves associated with Empoasca damage. Experimental results demonstrated that ResNet-50 outperformed AlexNet, achieving a remarkable accuracy of 99% on the green channel, while AlexNet showed notable accuracy declines on other channel combinations. These findings underscore the effectiveness of the green spectrum and the superiority of ResNet-50 in achieving precise pest detection, offering a reliable technological solution for modern tea plantation management.
Co-Authors . Hendrawan . Iskandar Fani Fauziah, Fani Fiky Y. Suratman Hurianti Vidyaningtyas Kurniawan, Bella K. Saputri, Desti M.