Penerapan Data Mining dalam Analisis Kejadian Banjir di Indonesia dengan Menggunakan Metode Association Rule Algoritma Apriori

Fifi Sanjaya, Muhammad Salahuddin, Lutfin Haryanto, Fitria Sarnita

Abstract


Floods are a natural disaster that frequently occurs in Indonesia. Disaster prevention measures and flood simulation and guidance are still less applied in other cities except for major cities, resulting in a low level of human safety. From the data obtained, many casualties and significant material losses were suffered by flood victims. Floods usually occur during the rainy season, but no one knows when and where they will happen. This study applies data mining techniques with association rules using the apriori algorithm to understand the patterns and association rules of flood events in Indonesia. The data were taken from the official website of the National Disaster Management Agency (BNPB) from 2014-2016 and analyzed using the R program. The association analysis results showed that "if there is flooding due to all survival factors, then there is a possibility of flooding in Cileunang" with support 38.7%, confidence 64.4%, and lift 1.0347319. Meanwhile, "if there is flooding in Cileunang, then there is a possibility of all surviving" with support 38.7%, confidence 64.4%, and lift 1.0347319.


Keywords


Data Mining, Association Rule, Apriori Algorithm, Flood Data, R Program

Full Text:

PDF

References


BNPB. (2017). Data Banjir. http://geospasial.bnpb.go.id/pantauanbencana/data/databanjirall.php. Diakses pada tanggal 14 Februari 2017.

Findayani, A. (2015). Kesiap siagaan masyarakat dalam penanggulangan banjir di Kota Semarang. Jurnal Geografi: Media Informasi Pengembangan Dan Profesi Kegeografian, 12(1), 102-114.

Kurniawan, H., & Fujiati, A. S. (2014). Analisa Pola Data Penyakit Rumah Sakit Dengan Menerapkan Metode Association Rule Menggunakan Algoritma Apriori. In Seminar Nasional Informatik.

Mujiasih, S. (2011). Pemanfatan Data Mining Untuk Prakiraan Cuaca. Jurnal Meteorologi dan Geofisika, 12(2).

Ramadhan, M. I., & Prihandoko, P. (2019). Penerapan Data Mining Untuk Analisis Data Bencana Milik BNPB Menggunakan Algoritma K-Means dan Linear Regression. Jurnal Ilmiah Informatika Komputer, 22(1).

Ristanto, N. I. (2017). Penentuan Pola Dampak Kerugian Banjir Menggunakan Metode Association Rule Dengan Algoritma Apriori (Studi Kasus: Kejadian Banjir di Indonesia Tahun 2015) (Doctoral dissertation, Universitas Islam Indonesia).

Setianingsih, D., & Hakim, R. B. (2015). Penerapan data mining dalam analisis kejadian tanah longsor di indonesia dengan menggunakan association rule algoritma apriori. UMS: Publikasi Ilmiah.

Siburian, B. R. (2014). Aplikasi Data Mining Untuk Menampilkan Tingkat Kelulusan Mahasiswa Dengan Algoritma Apriori. Pelita Informatika Budi Darma, 7(2), 56-61.

Suarmika, P. E., & Utama, E. G. (2017). Pendidikan mitigasi bencana di Sekolah Dasar (sebuah kajian analisis etnopedagogi). JPDI (Jurnal Pendidikan Dasar Indonesia), 2(2), 18-24.

Ulum, M. C. (2014). Manajemen bencana: Suatu pengantar pendekatan proaktif. Malang: Universitas Brawijaya Press.

Utari. (2015). Association Rule Menggunakan Algoritma Apriori untuk Analisis Pola Data Kecelakaan Pesawat dari Tahun 1967-2014 di Indonesia. UMS: Publikasi Ilmiah. UMS: Publikasi Ilmiah.




DOI: https://doi.org/10.56842/jp-ipa.v5i1.312

Refbacks

  • There are currently no refbacks.