The Natural Disaster Prone Index Map Model in Indonesia Using the Thiessen Polygon Method

  • Kevin Hendra William Universitas Kristen Satya Wacana
  • Kristoko Dwi Hutomo Universitas Kristen Satya Wacana
Abstract views: 1013 , PDF downloads: 619
Keywords: Polygon Thiessen, Indonesia, Natural Disaster, Mapping, System Information Geographic

Abstract

Natural Disasters are natural phenomena that occur at any moment that can cause loss. Indonesia is an archipelagic country located at the meeting of four tectonic plates and volcanic belts. This condition causes Indonesia to be prone to natural disasters. Therefore, it is necessary to make a natural disaster-prone index map model minimize the impact of natural disasters. In this research, the researchers used a Polygon Thiessen method for it was one of the mapping methods to determine a natural disaster based on Indonesia's vast surface and many disasters. The BNPB and Polygon Thiessen data comparison shows that BNPB data has a low level of vulnerability of 302, a moderate level of vulnerability of 148, and a high level of vulnerability of 58. In contrast, the Thiessen polygon has a low level of vulnerability of 297, a moderate vulnerability of 158, and a high vulnerability of 59. Comparing BNPB data and the Thiessen Polygon method found five differences from 40 data in the Papua region. Suggestions for further research to create an application-based information system so that it can be accessed in real-time.

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References

J. A. Nugroho, B. M. Sukojo, and I. Sari, “Pemetaan Daerah Rawan Longsor dengan Penginderaan Jauh dan Sistem Informasi Geografis,” ITS Libr., p. 9, 2009.

M. Restu; Damanik; Ridha S, “PEMETAAN TINGKAT RISIKO BANJIR DAN LONGSOR SUMATERA UTARA BERBASIS SISTEM INFORMASI GEOGRAFIS,” pp. 29–42, 2544.

“Data Informasi Bencana Indonesia (DIBI).” [Online]. Available: http://bnpb.cloud/dibi//tabel1a. [Accessed: 14-Apr-2020].

“Potensi Ancaman Bencana - BNPB.” [Online]. Available: https://bnpb.go.id/potensi-ancaman-bencana. [Accessed: 08-May-2020].

N. Novitasari, A. Nugraha, and A. Suprayogi, “Pemetaan Multi Hazards Berbasis Sistem Informasi Geografis Di Kabupaten Demak Jawa Tengah,” J. Geod. Undip, vol. 4, no. 4, pp. 181–190, 2015.

B. Gunadi, A. Nugraha, and A. Suprayogi, “Aplikasi Pemetaan Multi Risiko Bencana Di Kabupaten Banyumas Menggunakan Open Source Software Gis,” J. Geod. Undip, vol. 4, no. 4, pp. 287–296, 2015.

M. Farizki and W. Anurogo, “Pemetaan kualitas permukiman dengan menggunakan penginderaan jauh dan SIG di kecamatan Batam kota, Batam,” Maj. Geogr. Indones., vol. 31, no. 1, p. 39, 2017.

R. Rahmad, S. Suib, and A. Nurman, “Aplikasi SIG Untuk Pemetaan Tingkat Ancaman Longsor Di Kecamatan Sibolangit, Kabupaten Deli Serdang, Sumatera Utara,” Maj. Geogr. Indones., vol. 32, no. 1, p. 1, 2018.

M. Infromasi, P. Ilmu, P. Kegeografian, L. Di, and K. Kejajar, “Pemanfaatan Teknologi Sig Untuk Pemetaan Tingkat Ancaman Longsor Di Kecamatan Kejajar, Wonosobo,” Pemanfaat. Teknol. Sig Untuk Pemetaan Tingkat Ancaman Longsor Di Kec. Kejajar, Wonosobo, vol. 12, no. 2, pp. 202–213, 2015.

F. Faizana, A. Nugraha, and B. Yuwono, “Pemetaan Risiko Bencana Tanah Longsor Kota Semarang,” J. Geod. Undip, vol. 4, no. 1, pp. 223–234, 2015.

R. Pratiwi, A. Nugraha, and H. ah, “Pemetaan Multi Bencana Kota Semarang,” J. Geod. Undip, vol. 5, no. 4, pp. 122–131, 2016.

E. W. Weisstein, “Voronoi Diagram.”

B. Triatmodjo, “Hidrologi Terapan,” Beta Offset, 2008.

“GADM maps.” [Online]. Available: https://gadm.org/maps.html. [Accessed: 14-Aug-2020].

K. Lilik, R. Yunus, robi amir Muhammd, and P. Narwawi, “Indek Ks Rawa an Benc Cana in Ndones,” pp. 1–226, 2011.

K. D. Hartomo, J. P. Sri Yulianto, and E. Gumilanggeng, “Spatial model of koppen climate classification using thiessen polygon optimization algorithm,” J. Theor. Appl. Inf. Technol., vol. 96, no. 2, pp. 382–391, 2018.

S. Y. J. Prasetyo, “Model Prediksi Hujan dengan Kombinasi Metode Double Exponential Smooth, Thiessen Polygon, dan Isohyetal Wilayah Stasiun Iklim Jawa Tengah,” pp. 2–18, 2011.

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Published
2021-08-08
How to Cite
[1]
K. H. William and K. D. Hutomo, “The Natural Disaster Prone Index Map Model in Indonesia Using the Thiessen Polygon Method ”, intensif, vol. 5, no. 2, pp. 148-160, Aug. 2021.