Article Per Year (5 Year)
p-Index From 2021 - 2026
3.578
P-Index
This Author published in this journals
All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Computech & Bisnis (e-Journal) JETT (Jurnal Elektro dan Telekomunikasi Terapan) Emerging Science Journal Jurnal Sistem Cerdas TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat IJAIT (International Journal of Applied Information Technology) JMECS (Journal of Measurements, Electronics, Communications, and Systems) GUYUB: Journal of Community Engagement Prosiding Konferensi Nasional PKM-CSR SinarFe7 eProceedings of Engineering Journal of Computer Engineering: Progress, Application and Technology Jurnal Rekayasa elektrika Jurnal INFOTEL Jurnal Standardisasi Journal of Innovation and Community Engagement
Ratna Mayasari
Universitas Telkom
Religion Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Nursing Public Health Social Sciences Other

Published : 70 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : emerging science journal

 1 
QLAF: Q-Learning Adaptive Forwarding for Disaster Emergency Communication in Named Data Networking Ratna Mayasari; Galih N. Nurkahfi; Nana R. Syambas; Eueung Mulyana
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-03

Abstract

Reliable communication is essential for effective disaster response; however, conventional IP-based networks often fail when the network infrastructure is damaged. Disaster communication networks need adaptive forwarding strategies that maintain reliability under rapid topology changes, various link qualities, and resource constraints. This research proposes a Q-Learning-based Adaptive Forwarding (QLAF) strategy designed to enhance reliability in heterogeneous disaster emergency communication networks. QLAF implements reinforcement learning into the NDN forwarding plane, enabling each router to autonomously learn optimal forwarding faces based on multiple performance metrics: Round-Trip Time (RTT), throughput, and link stability. The proposed strategy was implemented in the Named Data Networking Forwarding Daemon (NFD) and evaluated using the MiniNDN emulator over a BRITE-generated 25-node disaster topology that integrates terrestrial, cellular, and satellite links. We compared QLAF and Adaptive Smoothed RTT-based Forwarding (ASF), Access strategy, and Self-Learning. Experimental results show that QLAF achieves a Packet Delivery Ratio (PDR) of 99.91%. These results show that QLAF gives a robust solution for reliability-sensitive disaster communication, guaranteeing high data delivery performance under unstable network conditions. However, its latency overhead limits its applicability to real-time scenarios.
Dual-Agent Q-Learning for Cross-Layer IEEE 802.11bd Optimization in Dense VANETs Galih Nugraha Nurkahfi; Suyoto; Agus Subekti; Budi Prawara; Ratna Mayasari; Andy Triwinarko; Nasrullah Armi; Eueung Mulyana; Nana Rachmana Syambas
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-019

Abstract

Dense vehicular ad hoc networks face critical challenges in reliably delivering safety messages due to channel congestion, packet collisions, and interference. This study develops a dual-agent Q-learning framework for cross-layer IEEE 802.11bd optimization to improve latency and power efficiency while maintaining acceptable packet delivery ratios in dense traffic. We propose a decomposed architecture separating PHY-layer power control and MAC-layer beacon rate adaptation, with deterministic SINR-based MCS selection ensuring IEEE 802.11bd compliance. The framework is evaluated using a Python-based VANET simulator implementing the IEEE 802.11bd PHY/MAC stack with realistic SUMO mobility, multi-class background traffic, and omnidirectional/sectoral antennas across 20-90 vehicles/km densities. Results show dual-agent Q-learning reduces average latency by 44.6% (31.1ms to 17.2ms) and transmission power by 55% (15-20dBm to 9dBm) compared to static baselines, with acceptable 5-11% PDR reduction (94.2% to 88.6%). The approach converges within 8,500 episodes, significantly faster than single-agent Q-learning (12,500) and dual-agent DQN (14,000-35,000). This work introduces the first dual-agent tabular Q-learning for joint power-rate-MCS optimization in IEEE 802.11bd VANETs, demonstrating that agent decomposition reduces state-action complexity while enabling interpretable, fast-converging control suitable for sub-100ms vehicular applications.
Co-Authors Achmad Mustofa Luthfi Ade Nurhayati, Ade Adi Sucipto, Adi Adith Priyo Pratama Aditya Bram Wiratma Aditya Kurniawan Agtusia, Arwidya Tantri Agus Subekti Ahmad Abdillah Siddiq Ahmad Fauzan Jaya Ahmad Mudhoffar Rabbani Ahmad Mudhoffar Rabbani Ahmad Saifuddin Mufid Ahmad Thoriq Azzam Ahmad Tri Hanuranto Aidil Afdan Pananrang Alifiyah Pratiwi P.Wedda AM, Debby Tri Wulandari Amelia Putri Anggadhini Andarwati, Sari Andy Triwinarko Ardhi Anzala Muhammad Ari Satrio Arif Faturrachman Arif Indra Irawan Azella Nanda Yudri Azella Nanda Yudri Azella Nanda Yudri Azwar Hamsir Azzam, Ahmad Thoriq Bahtiar Widayanto Basuki Rahmat Brando, Robby Marlon Budi Prawara Budi Syihabuddin Chrisna Fiddin Danu Dwi Sanjoyo Dawam Dwi Jatmiko Suwawi Devi Fitriani Devyta Asterina Dimas Surya Putra Dimitri Mahayana Dini Adlina Salman DWI ANDI NURMANTRIS Dwima Anggraini Eki Ahmad Zaki Hamidi Elfarizi, Sayid Huseini Ellia Kristiningrum Erna Sri Sugesti Eueung Mulyana Faishal Yusuf Baqir Fakhri Rahmatullah Faturrachman, Arif Fransisca Elisa Rahardjo Galih N. Nurkahfi Galih Nugraha Nurkahfi Galih Nugraha Nurkahfi Gregorius Pradana Satriawan Hafidudin . Hasan Nur Arifin Hasan Nur Arifin Hasnah Faradina Nur Ilham Hurianti Vidyaningtyas Ibnu Asror Ida Wahidah Hamzah Indrarini Irawati Iwan Iwut Tritoasmoro Jupriyadi Jupriyadi Jupriyadi, Jupriyadi Kharisma Bani Adam Leanna Vidya Yovita M, Fajri Ismail Mario Putra Masriza Kurniawan Maya Rosalia Mochamad Adhi Pratama Muhammad Ary Murti Muhammad Eko Wahyudi Muhammad Rizki Utomo Muhammad Rizky Ramzani Muhammad, Zaid Nabila Nabila Nadya Dwi Aulia Nadya Moeliono Nana R. Syambas Nana Rachmana Syambas Nana Rachmana Syambas Nana Rachmana Syambas Nanda Cahyangtia Pratama Nasrullah Armi Naufal Dzulfiqar Anwar Nindithia Putri Windryani Nyoman Bogi Aditya Karna Pramitha Aulia Rahmatullah, Fakhri Ratih Loviesta Nurbed Rendy Munadi Reza Nuryati Riany Erdiyanti Ridha Muldina Negara Rivaldy Arif Pratama Riyo Surya Putra Rizal Erwin Irwansyah Rizqi Surya Utama ROHMAT TULLOH Safitri, Nesya Alvioni Salwa Aulia Farida Y Sayid Huseini Elfarizi Septhian Dwi Putra Prabowo Setia Juli Irzal Ismail Sofia Naning Hertiana Sri Astuti Sri Astuti Sugondo Hadiyoso Suyoto Syaiful Ahdan Syaiful Ahdan Tiara Tiara Tody Ariefianto Wibowo Toni Kusnandar Tuntun Aditara Maharta Vikry Fadillah Wasesa, Novan Purba Wildan Yoga Swara Xavier Samuel MFP Yanuar Firdaus Yeni Sanovia YULI SUN HARIYANI