Disasterwatch: Android-Based Implementation of Yogyakarta Natural Disaster Monitoring Prediction

The increasing threat of natural disasters in Yogyakarta, Indonesia, calls for technological solutions to predict and monitor disaster events more accurately. The development of the DisasterWatch application aims to provide a reliable disaster prediction tool, using machine learning and data mining-based predictive modeling methods. The app utilizes various machine learning algorithms to analyze historical disaster data as well as related risk factors, such as weather, seismic activity and soil conditions. Preliminary results show that this prediction system can improve the accuracy of early warnings, but further development is needed to ensure its accessibility and reliability for the community. In addition, to ensure its accessibility and reliability for the wider community, especially in disaster-prone areas. Collaboration with BMKG, and local communities is being sought to ensure this application can be practically implemented in the field

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Publication Details

Journal
INOVTEK Polbeng - Seri Informatika
Published
2024-10-24
DOI
https://doi.org/10.35314/70vtet38
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Disasterwatch: Android-Based Implementation of Yogyakarta Natural Disaster Monitoring Prediction

Musthafa Anggoro, Suhirman Suhirman
INOVTEK Polbeng - Seri Informatika
Data Mining and Machine Learning Applications
article

Disasterwatch: Android-Based Implementation of Yogyakarta Natural Disaster Monitoring Prediction

Musthafa Anggoro, Suhirman Suhirman
article en

Abstract

The increasing threat of natural disasters in Yogyakarta, Indonesia, calls for technological solutions to predict and monitor disaster events more accurately. The development of the DisasterWatch application aims to provide a reliable disaster prediction tool, using machine learning and data mining-based predictive modeling methods. The app utilizes various machine learning algorithms to analyze historical disaster data as well as related risk factors, such as weather, seismic activity and soil conditions. Preliminary results show that this prediction system can improve the accuracy of early warnings, but further development is needed to ensure its accessibility and reliability for the community. In addition, to ensure its accessibility and reliability for the wider community, especially in disaster-prone areas. Collaboration with BMKG, and local communities is being sought to ensure this application can be practically implemented in the field

INOVTEK Polbeng - Seri InformatikaVol. 9(2)
Universitas Teknologi Yogyakarta (ID)
Climate action
Openalex Percentile: Top 67%
Data Mining and Machine Learning Applications
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Disasterwatch: Android-Based Implementation of Yogyakarta Natural Disaster Monitoring Prediction — Musthafa Anggoro, Suhirman Suhirman · INOVTEK Polbeng - Seri Informatika (2024) | TGRS Research Map | TGRS