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All Journal JURNAL HUTAN LESTARI Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Journal of Tropical Soils Emerging Science Journal
. Iskandar
Library & Information Science Program Interdiciplinary Islamic Studies, Graduated School of Islamic University Sunan Kalijaga Yogyakarta, Indonesia
Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Other

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Journal : Emerging Science Journal

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Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting Juhana, Tutun; Yuliana, Hajiar; Hendrawan, .; Iskandar, .; Musashi, Yasuo
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-04

Abstract

This study evaluates the performance of three forecasting models—ARIMA, Prophet, and Glmnet—with the primary objective of equipping the telecommunication industry with effective tools for cellular traffic forecasting. These tools lay the foundation for efficient resource management, cost optimization, and enhanced service delivery. The study begins with dataset description and preparation, followed by the selection of traffic forecasting models, and concludes with performance evaluation based on metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The main contribution of this research is a comprehensive comparison of the three forecasting methods, aiding practitioners and researchers in identifying the best prediction model for specific contexts. The findings reveal that Glmnet consistently outperforms ARIMA and Prophet across all categories of traffic forecasting on the selected performance metrics. Its ability to handle complex data structures, manage multicollinearity, and deliver robust and accurate predictions makes it the preferred choice for forecasting cellular network traffic in the telecommunications domain. Doi: 10.28991/ESJ-2024-08-06-04 Full Text: PDF
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
Co-Authors . Hendrawan . Suwardi Ade Mariyam Oklima Ade Mariyam Oklima Dyah Tjahyandari Suryaningtyas Erlinda, Asteria Hurianti Vidyaningtyas Juita, Nirmala Mukti, Rino Rakhmata Musashi, Yasuo Pramudita, Aloysius A. Priyadi, . Sudarsono Tri Widiastuti TUTUN JUHANA Yuliana, Hajiar