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All Journal Sinergi Communications in Science and Technology
Iqbal, Farabi
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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An intelligent approach for detection and classification of security attacks in a Passive Optical Network using Light Gradient Boosting Machine Bibi, Sumayya; Zulkifli, Nadiatulhuda; Iqbal, Farabi; Iqbal, Sajid; Ramli, Arnidza; Yoon Khang, Adam Wong
SINERGI Vol 29, No 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.3.005

Abstract

Over the past decade, Passive Optical Networks (PONs) have emerged as a leading solution for next-generation broadband access, providing high-speed and cost-effective communication. However, PONs face significant security challenges, including data interception, denial-of-service (DoS) attacks, and resource exhaustion caused by malicious Optical Network Units (ONUs). Machine learning (ML), particularly advanced models like Light Gradient Boosting Machine (LightGBM), has proven to be a promising solution for managing complex security issues in PONs. Leveraging its ability to handle imbalanced, high-dimensional datasets, LightGBM was employed in this study to detect and classify malicious ONUs based on bandwidth usage patterns. The model achieved an impressive accuracy of 95.27%, a Matthews Correlation Coefficient (MCC) of 90%, and a precision rate of 93%. While traditional classifiers, such as Naïve Bayes (NB), achieved an accuracy of 88.53%, LightGBM demonstrated superior robustness in addressing class imbalance and enhancing detection accuracy. This work highlights the potential of LightGBM in enhancing PON security and enabling intelligent, resilient broadband networks.
Material Properties Extraction of Mango (Mangifera indica) Leaves at Ka-Band Using a Waveguide Measurement System Arisesa, Hana; Idrus, Sevia Mahdaliza; Iqbal, Farabi; Abdullah, M.F.L; Wijayanto, Yusuf Nur; Adhi, Purwoko
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1768

Abstract

This study investigates the material properties (permittivity, dissipation factor, and conductivity) of mango leaves (Mangifera indica) over the 26–40 GHz Ka-band frequency based on a waveguide measurement system with a vector network analyzer instrument to capture the data. The data analysis employs the Nicolson-Ross-Weir method to extract material properties. The result reveals that the real part of permittivity decreases from about 11.0 to 5.0 with increasing frequency. Meanwhile, the imaginary part of permittivity remains low and stable, suggesting minimal absorption losses. The dissipation factor is consistently below 0.05 along the band. Effective conductivity ranges from 0.2 to 0.6 S/m, with a slight increase at higher frequencies. These findings suggest that at Ka‐band frequency, signal degradation through mango foliage is primarily driven by dispersion and scattering rather than strong dielectric absorption. The results provide essential information for improving foliage attenuation models and designing 5G and 6G communication systems in tropical regions. This study provides a reliable Ka-band dielectric dataset for mango leaves that improves the accuracy of tropical foliage-attenuation models and supports more robust 5G/6G link design and deployment in vegetation-dense environments.
Co-Authors Arisesa, Hana Bibi, Sumayya Iqbal, Sajid M.F.L Abdullah Purwoko Adhi Ramli, Arnidza Sevia Mahdaliza Idrus Yoon Khang, Adam Wong Yusuf Nur Wijayanto, Yusuf Nur Zulkifli, Nadiatulhuda