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All Journal Simtek : Jurnal Sistem Informasi dan Teknik Komputer INTI Nusa Mandiri
Gilang Andhika Buwana
Universitas Logistik dan Bisnis Internasional
Computer Science & IT Electrical & Electronics Engineering Engineering

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KLASIFIKASI KONDISI CUACA JALAN RAYA BERBASIS CITRA CCTV MENGGUNAKAN VISION TRANSFORMER Gilang Andhika Buwana; Mohamad Nurkamal Fauzan
Simtek : jurnal sistem informasi dan teknik komputer Vol. 11 No. 1 (2026): April 2026
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v11i1.1719

Abstract

Kondisi cuaca, khususnya hujan, merupakan faktor penting yang memengaruhi keselamatan dan kelancaran lalu lintas jalan raya. Informasi cuaca yang tersedia saat ini umumnya bersifat regional dan belum sepenuhnya merepresentasikan kondisi aktual di lokasi tertentu secara real-time. Penelitian ini bertujuan untuk mengklasifikasikan kondisi cuaca jalan raya berbasis citra CCTV menggunakan Vision Transformer (ViT). Dataset yang digunakan merupakan gabungan citra CCTV jalan raya dan dataset DAWN untuk mengatasi permasalahan ketidakseimbangan kelas. Dataset akhir terdiri atas dua kelas, yaitu cerah dan hujan, dengan jumlah masing-masing 325 citra. Seluruh citra melalui tahapan preprocessing berupa resizing, normalisasi, dan augmentasi data pada data latih. Model Vision Transformer base patch16-224 dengan bobot pretrained digunakan dengan skema pembekuan backbone dan pelatihan lapisan classifier selama 15 epoch. Hasil evaluasi menunjukkan model mencapai akurasi sebesar 88% dengan nilai precision, recall, dan F1-score yang seimbang pada kedua kelas. Hasil ini menunjukkan bahwa Vision Transformer efektif digunakan untuk klasifikasi kondisi cuaca jalan raya berbasis citra CCTV dan berpotensi dikembangkan sebagai sistem informasi cuaca berbasis visual yang mendekati real-time.
TINJAUAN LITERATUR SISTEMATIS KLASIFIKASI CUACA BERBASIS CITRA MENGGUNAKAN METODE DEEP LEARNING Gilang Andhika Buwana; Mohamad Nurkamal Fauzan
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8615

Abstract

Image-based weather recognition has become an important research area due to its applications in intelligent transportation systems, autonomous vehicles, road condition monitoring, and computer vision. Recent advances in deep learning have significantly improved the automatic recognition of diverse weather conditions. However, the variety of image sources, datasets, deep learning architectures, and weather scenarios makes it challenging to obtain a comprehensive understanding of current research trend. Therefore, this study aims to identify the image sources and datasets used in weather recognition research, analyze dominant deep learning approaches, and investigate the weather conditions most frequently addressed in image-based weather recognition and road weather detection. A Systematic Literature Review (SLR) following the PRISMA 2020 guidelines was conducted using the Scopus database. From an initial set of 603 retrieved articles, 73 studies met the predefined inclusion criteria and were selected for further analysis. The results indicate that public datasets such as DAWN, RTTS, Foggy Cityscapes, nuScenes, WeatherDataset-4, and WeatherNet are among the most frequently used data sources. Convolutional Neural Networks (CNNs) remain the dominant approach, although the adoption of Transformer-based models, Vision Transformers, YOLO, Multimodal Fusion, and Multi-Task Learning has increased considerably in recent years. Furthermore, rain, fog, and snow are the most extensively investigated weather conditions due to their significant impact on visibility and perception system performance. The findings provide a comprehensive overview of recent developments in deep learning-based weather recognition and offer valuable insights and directions for future research.
Co-Authors Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan