Keywords
Fire Risk Level
Google Maps API
Abstract
Fire disasters occur due to triggering factors originating from the source of the fire and triggering factors from the surrounding environment, for example, it can be seen based on the quality of building materials, settlement patterns, and the distance between buildings that are close to each other. This can be used as a basis for recognizing the level of risk of a residential area against a fire disaster. In this study, the level of fire risk is calculated based on the variables of threat (hazard), vulnerability, and resilience (capacity).
This study aims to produce a geographic information system that can map the level of risk of residential fires in Banjarmasin City by utilizing the Google Maps API. Mapping the level of disaster risk is carried out as a mitigation effort to reduce the risk or loss caused by disasters, especially fire disasters in urban settlements.
The result of this research is a Sistem Informasi Geografis Zonasi Tingkat Risiko Kebakaran Permukiman (SI TRISKA) which can map and calculate the level of fire risk based on the variables of threat, vulnerability, and resilience in a residential area.
References
2. Aronoff, Stanley. 1989. Geographic Information Systems: A Management Perspective. Ontario, Canada: WDL Publications.
3. Asrani. 2018. Aplikasi Pemetaan Objek Bangunan Pada Sekretariat Daerah Kabupaten Tabalong Berbasis GIS Bentuk Piramida Terbalik. Skripsi. Banjarmasin: Jurusan Teknik Informatika. STMIK Indonesia Banjarmasin
4. Badan Pusat Statistik. 2019. Kota Banjarmasin Dalam Angka 2019. Banjarmasin: BPS Kota Banjarmasin.
5. Badan Pusat Statistik. 2019. Statistik Daerah Kota Banjarmasin 2019. Banjarmasin: BPS Kota Banjarmasin.
6. Google Developers. 2019. Maps JavaScript API [Internet]. Tersedia pada: https://developers.google.com/maps/documentation/javascript/tutorial. (diakses 10 November 2019)
7. Laboratorium Geologi Dinamik. 2016. Mitigasi Bencana Geologi. [Internet]. Tersedia pada: https://mitgeo.ft.ugm.ac.id/artikel/. (diakses 12 November 2019)
8. Laboratorium Geologi Dinamik. 2016. Penginderaan Jauh dan Sistem Informasi Geografis (SIG) dalam Manajemen Bencana Alam. [Internet]. Tersedia pada: http://mitgeo.ft.ugm.ac.id/2016/08/06/test-4/. (diakses 12 November 2019)
9. Jalil, Ahmad M. 2014. Urban Fire Risk Assessment Using GIS: Case Study on Sharjah, UAE. International Geoinformatics Research and Development Journal. 5(3): 1-7.
10. Miles, Russ, dkk. 2006. Learning UML 2.0: A Pragmatic Introduction to UML. USA: OReilly Media
11. Noermala, Windy. 2009. Evaluasi dan Analisis Konsekuensi Alat Pemadam Api Ringan (APAR) di Gedung A FKM UI Tahun 2009 Dengan Metode Event Tree Analysis, Skripsi. Depok: Fakultas Kesehatan Masyarakat. Universitas Indonesia
12. Rendi, dkk. 2014. Analisis Zonasi Daerah Rawan Bencana Kebakaran Di Kecamatan Danurejan Kota Yogyakarta Dengan Menggunakan Citra Satelit Quickbird Dan Sistem Informasi Geografi, Skripsi. Surakarta: Fakultas Geografi. Universitas Muhammadiyah Surakarta.
13. Santriadi, Bayu. 2012. Perancangan Aplikasi Pemetaan Sungai Di Kabupaten Barito Kuala Untuk Mengatasi Rawan Banjir Berbasis Sistem Informasi Geografis. Skripsi. Banjarmasin: Jurusan Teknik Informatika. STMIK Indonesia Banjarmasin
14. Tim SGTGeomedia. 2017. Membuat WebGIS Sederhana Menggunakan Google Maps API, MS4W, PostgreSQL & PostGIS. SGTGeomedia.
15. Widyantoro, Bimo Aji. 2016. Analisis Tingkat Resiko Bencana Kebakaran Di Kecamatan Mariso Kota Makassar Berbasis Sistem Informasi Geografis (SIG), Skripsi. Makassar: Fakultas Sains dan Teknologi. UIN Alauddin Makassar.