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Journal : JOIN (Jurnal Online Informatika)

Performance Evaluation of Vehicular Ad Hoc Networks Considering Malicious Node Impact on Quality of Services Metrics Alfarizi, Naufal Faiz; Nuruzzaman, Muhammad Taufiq; Uyun, Shofwatul; Sugiantoro, Bambang; Abdullah, Mohd. Fikri Azli bin
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1568

Abstract

Vehicular Ad Hoc Networks (VANETs), a subset of mobile ad hoc networks (MANETs), is essential for enabling communication between vehicles in intelligent transportation systems. However, their dynamic and decentralized nature exposes them to significant security threats, particularly from malicious nodes. Attacks such as black holes and wormholes can severely degrade network performance by causing packet loss and increasing end-to-end delays. This paper aims to evaluate the impact of malicious node behavior on VANET performance using key Quality of Service (QoS) parameters, including throughput, end-to-end delay, jitter, packet delivery ratio (PDR), and packet loss ratio (PLR). The specific objective is to analyze how black hole and wormhole attacks affect communication efficiency in VANET environments. The main contribution of this work lies in the integration of Simulation of Urban Mobility (SUMO) for realistic traffic scenario generation with Network Simulator 3 (NS-3) for detailed network performance evaluation. This approach enables comprehensive simulation of VANET behavior under attack conditions. The findings provide valuable insights into the vulnerabilities of VANETs and form a basis for the design of more robust and secure vehicular communication systems.
Land Cover Classification in Mountainous Regions Using Multi-Scale Fusion and Convolutional Neural Networks: A Case Study on Mount Slamet Yulis Rijal Fauzan; 'Uyun, Shofwatul
JOIN (Jurnal Online Informatika) Vol 10 No 2 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i2.1612

Abstract

Mount Slamet, located in Central Java, Indonesia, is a high-risk volcanic region where accurate land cover classification is essential for disaster mitigation and sustainable land management. However, satellite imagery in this area often suffers from haze and cloud cover, posing challenges to reliable classification. This study aims to develop an effective land cover classification model using Sentinel-2 imagery by addressing these visual distortions. The specific goal is to classify land cover into five classes—Forest, Settlements, Summit, RiceField, and River—using enhanced satellite images. A total of 1101 labeled images were processed through dehazing with Multi-Scale Fusion (MSF) and smoothing using a Guided Filter to improve image quality. The classification was performed using three Convolutional Neural Network (CNN) architectures: VGG-16, MobileNetV2, and DenseNet121. The main contribution of this study is the integration of a tailored preprocessing pipeline with CNN-based modeling for haze-affected mountainous satellite imagery. Among the models tested, MobileNetV2 achieved the highest accuracy of 85.4%, outperforming DenseNet121 (83.8%) and VGG-16 (82.3%). The results demonstrate the effectiveness of combining image enhancement techniques with lightweight CNN architectures for land cover classification in challenging environments with limited and imbalanced dataset.