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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer Format : Jurnal Imiah Teknik Informatika bit-Tech Jurnal Teknik Informatika (JUTIF) Jurnal Kajian Ilmiah Nusantara Hasana Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Gema Lingkungan Kesehatan Journal Information & Computer (JICOM) Jurnal Relawan dan Pengabdian Masyarakat REDI Jurnal Pengabdian kepada Masyarakat The Scientific Journal of Information Systems Fortech : Journal of Information Technology
Surahmat, Asep
Universitas Utpadaka Swastika
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Pemanfaatan Generative Artificial Intelligence (GenAI) untuk Prediksi dan Analisis Bencana Alam Arief Wibowo; Asep Surahmat
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Disaster prediction and analysis are crucial components in mitigating the impacts of natural hazards such as floods, earthquakes, and landslides. Conventional systems often rely on deterministic models and limited historical data, which restrict their accuracy and adaptability to dynamic environmental changes. The emergence of Generative Artificial Intelligence (GenAI), particularly models based on deep learning and generative architectures such as Generative Adversarial Networks (GANs) and Diffusion Models, introduces new opportunities for synthetic data generation and predictive simulation. This study aims to explore the implementation of GenAI in disaster prediction and analysis by reviewing recent literature and practical applications in Indonesia. The proposed framework integrates multimodal data—including meteorological, seismic, and remote sensing data—into generative models to simulate disaster scenarios and improve early warning systems. The results indicate that GenAI can enhance data diversity, reduce bias in model training, and support real-time decision-making in disaster management. The study concludes that GenAI has strong potential to revolutionize disaster analytics and strengthen climate resilience through adaptive, data-driven insights. Thus, the output of this research is conceptual and focuses on designing a framework, while empirical testing forms the basis for further research development.
Co-Authors Alfaujianto, Moh Alfaujianto, Moh. Arief Wibowo Darmawan, Rizqi Dhimas Buing Rindi Widra Yato Didi Kurnaedi Erna Oktora Fahmi Nugraha, Fahmi Fajar Muttaqi Fajar Muttaqi Grace Limiko Hasyanta, Gopas Indra Indra Indra Lukas Umbu Zogara Manorang Sihotang Moh Alfaujianto Moh. Alfaujianto Mutiarawan, Rezza Anugrah Muttaqi, Fajar Nurul Badriah Pupista, Orinda Salma, Triana Dewi Sutrisno, Mirza Yato, Dhimas Buing Rindi Widra