Amril Mutoi Siregar


Indonesia is a country located in the equator, which has beautiful natural. It has a mountainous constellation, beaches and wider oceans than land, so that Indonesia has extraordinary natural beauty assets compared to other countries. Behind the beauty of natural it turns out that it has many potential natural disasters in almost all provinces in Indonesia, in the form of landslides, earthquakes, tsunamis, Mount Meletus and others. The problem is that the government must have accurate data to deal with disasters throughout the province, where disaster data can be in categories or groups of regions into very vulnerable, medium, and low disaster areas. It is often found when a disaster occurs, many found that the distribution of long-term assistance because the stock for disaster-prone areas is not well available. In the study, it will be proposed to group disaster-prone areas throughout the province in Indonesia using the k-means algorithm.

The expected results can group all regions that are very prone to disasters. Thus, the results can be Province West java, central java very vulnerable categories, provinces Aceh, North Sumatera, West Sumatera, east Java and North Sulawesi in the medium category, provinces Bengkulu, Lampung, Riau Island, Babel, DIY, Bali, West Kalimantan, North Kalimantan, Central Sulawesi, West Sulawesi, Maluku, North Maluku, Papua, west Papua including of rare categories. With the results obtained in this study, the government can map disaster-prone areas as well as prepare emergency response assistance quickly. In order to reduce the death toll and it is important to improve the services of disaster victims. With accurate data can provide prompt and appropriate assistance for victims of natural disasters.


Algoritma K-means; Clustering; Data Mining; Bencana Alam

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Agusta., 2007., Implementasi Algoritma Clustering Dengan Singuler Vector Decomposition

Untuk Menunjang Keputusan Dalam Meningkatkan Produktivitas Tanaman Jagung.,

Denpasar., bali.

Connoly, T., & Begg, c., 2005., A practical approach to design, implementation and

management (4th ed.)., Harlow Addison Wesley.

Nugraha et al., 2013. Penyusunan dan Penyajian Peta online risiko bencana banjir rob di kota

semarang., Yogyakarta., UGM.

Nugroho et al., 2009., Pemetaan Daerah Rawan Longsor dengan Penginderaan Jauh dan Sistem

Informasi Georafis., Surabaya., ITS.

Siregar AM, 2018., Implementasi algoritma clustering dengan singuler vector decomposition

untuk menunjang keputusan dalam meningkatkan produktivitas tanaman jagung.,


Tan, P.N., Steinbech, M.Kumar, V. 2006., Introduction to data mining. Boston., Pearsong

Education., Ltd

Wu, X.,Kumar, V., 2009., The top ten algorithms in data mining. Boca Raton.,,CRC Press



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