Revolusi Sistem Transportasi Cerdas: AODV Berbasis Learning Automata untuk Peningkatan Komunikasi V2V di Jalan Bebas Hambatan - Bryan Jonathan Hutapea; Ketut Bayu Yogha Bintoro; Helna Wardhana (Jurnal Bumigora Information Technology (BITe), 2025)
Backgroud: Vehicle-to-vehicle communication has become a crucial element in the development of intelligent transportation systems. However, conventional routing protocols face limitations in coping with dense and dynamic traffic conditions. Objective: The objective of this study is to improve communication efficiency between vehicles by modifying an on-demand routing protocol using a learning automata approach. Method: This study employed a simulation method with traffic modeling using traffic modeling software and network simulation tools, based on data from highways in the Soekarno-Hatta International Airport area. Result: The results of this study show that the developed protocol increases the packet delivery ratio to 87.7% and reduces latency by 6.5%. Conclusion: The conclusion of this study is that the application of learning automata in vehicle routing enhances communication reliability and supports the implementation of a more adaptive and efficient transportation system.