CLUSTERING ZONASI DAERAH RAWAN BENCANA ALAM DI PROVINSI SUMATERA BARAT MENGGUNAKAN ALGORITMA K-MEANS DAN LIBRARY GEOPANDAS
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May 10, 2023
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Indonesia is one of the countries that are often affected by natural disasters because it is located on the Asia Pacific Ring of Fire. West Sumatra province is one of the most frequently hit by natural disasters including earthquakes. According to the Central Bureau of Statistics of West Sumatra from 2018-2021, there were 1,148 earthquake cases and 1,503 flood cases. The objective of this study is to reveal areas prone to natural disasters in West Sumatra province using K-Means algorithm and GeoPandas visualization. After doing data processing for the optimal K value, 3 clusters were obtained, namely, cluster 1 is not prone to natural disasters, cluster 2 is prone to natural disasters and cluster 3 is very prone to natural disasters. From this research, it is found that cluster 1 has 7 members of the region, cluster 2 has 11 members of the region, and cluster 3 has 1 member of the region.
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Wicaksono, D. A., & Susetyo, Y. A. (2023). CLUSTERING ZONASI DAERAH RAWAN BENCANA ALAM DI PROVINSI SUMATERA BARAT MENGGUNAKAN ALGORITMA K-MEANS DAN LIBRARY GEOPANDAS. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 4(2), 426-438. https://doi.org/10.35870/jimik.v4i2.225
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Findayani, A., 2018. Kesiap siagaan masyarakat dalam penanggulangan banjir di Kota Semarang. Jurnal Geografi: Media Informasi Pengembangan Dan Profesi Kegeografian, 12(1), pp.102-114.
Metrikasari, R. and Choiruddin, A., 2021. Pemodelan Risiko Gempa Bumi di Pulau Sumatera Menggunakan Model Inhomogeneous Neyman-Scott Cox Process. Jurnal Sains dan Seni ITS, 9(2), pp.D102-D107. DOI: https://doi.org/10.12962/j23373520.v9i2.52318.
BPS Provinsi Sumatera Barat. https://sumbar.bps.go.id/.
Firdaus, H., 2022. Analisa Cluster Menggunakan K-Means Dan Fuzzy C-Means Dalam Pengelompokan Provinsi Menurut Data Intesitas Bencana Alam Di Indonesia Tahun 2017-2021. MATHunesa: Jurnal Ilmiah Matematika, 10(1), pp.50-60. DOI: https://doi.org/10.26740/mathunesa.v10n1.p50-60.
Suwardi, A.M., Susetyo, B. and Hermawan, E., 2019, October. Analisa Spasial Clustering Zonasi Rawan Bencana Tanah Longsor Wilayah Bogor Selatan Berbasis WebGIS. In Seminar Nasional Teknologi Informasi (Vol. 2, pp. 182-190).
Kurniadewi, H., Hakim, R.A., Jajuli, M. and Jaman, J.H., 2022. Pemetaan UMKM dalam Upaya Pengentasan Kemiskinan dan Penyerapan Tenaga Kerja Menggunakan Algoritma K-Means. Journal of Applied Informatics and Computing, 6(2), pp.113-119. DOI: https://doi.org/10.30871/jaic.v6i2.4227.
Murdiaty, M., Angela, A. and Sylvia, C., 2020. Pengelompokkan Data Bencana Alam Berdasarkan Wilayah, Waktu, Jumlah Korban dan Kerusakan Fasilitas Dengan Algoritma K-Means. Jurnal Media Informatika Budidarma, 4(3), pp.744-752. DOI: http://dx.doi.org/10.30865/mib.v4i3.2213.
Sihombing, R.E., Rachmatin, D. and Dahlan, J.A., 2019. Program Aplikasi Bahasa R Untuk Pengelompokan Objek Menggunakan Metode K-Medoids Clustering. Jurnal EurekaMatika, 7(1), pp.58-79.
Heraldi, H.Y., Aprilia, N.C. and Pratiwi, H., 2019. Analisis Cluster Intensitas Kebencanaan di Indonesia Menggunakan Metode K-Means. Indonesian Journal of Applied Statistics, 2(2), pp.137-144. DOI: https://doi.org/10.13057/ijas.v2i2.34911.
Dewi, D.A.I.C. and Pramita, D.A.K., 2019. Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali. Matrix: Jurnal Manajemen Teknologi Dan Informatika, 9(3), pp.102-109. DOI: https://doi.org/10.31940/matrix.v9i3.1662.
Geopandas. https://geopandas.org/en/stable/
Yim, A., Chung, C. and Yu, A., 2018. Matplotlib for Python Developers: Effective techniques for data visualization with Python. Packt Publishing Ltd.
Fuadi, W., Meliala, S. and Fariadi, D., 2021. Rancang Bangun Aplikasi Game Pembelajaran Pseudocode Dasar. JET (Journal of Electrical Technology), 6(1), pp.31-34.