Clustering Flood Prone Areas in Deli Serdang Regency Using Density-Based Spatial Clustering Of Application With Noise (DBSCAN) Method
Abstract
Deli Serdang Regency is the most frequently flooded area in North Sumatra Province, causing many casualties and other losses to residents in flooded areas. Deli Serdang Regency has 22 sub-districts, each of which has a different level of flood vulnerability. Efforts are needed to categorize the level of flood vulnerability that needs to be watched out for in Deli Serdang Regency. The clustering used in this research is Density-Based Spatial Clustering Applications with Noise (DBSCAN). The purpose of this study is to determine the level of proneness to flooding in each region in 2022 in Deli Serdang Regency. The clustering results in this study concluded that using the DBSCAN algorithm, we obtained 2 clusters and 4 noise with a silhouette coefficient value of 0.395050089 with Epsilon 1.19 and MinPts 3. From the silhouette coefficient results, it can be concluded that the cluster structure obtained is weak because, with more variables, the calculation of distance based on density becomes invalid.
Keywords
Full Text:
PDFReferences
Anggraini, N., Pangaribuan, B., Siregar, A. P., Sintampalam, G., Muhammad, A., Damanik, M. R. S., & Rahmadi, M. T. (2021). Analisis pemetaan daerah rawan banjir di kota medan tahun 2020. Jurnal Samudra Geografi, 4(2), 27–33. https://doi.org/10.33059/jsg.v4i2.3851
Aprillya, M. R., & Chasanah, U. (2021). Analisis lahan pertanian rawan banjir menggunakan metode multi atribut utility theory berbasis sistem informasi geografis. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 16(2). https://doi.org/10.30872/jim.v16i2.6554
Badan Pusat Statistika. (2023). Kabupaten deli serdang dalam angka. https://deliserdangkab.bps.go.id/publication.html?page=3
Cholil, S. R., Handayani, T., Prathivi, R., & Ardianita, T. (2021). Implementasi algoritma klasifikasi k-nearest neighbor (knn) untuk klasifikasi seleksi penerima beasiswa. IJCIT (Indonesian Journal on Computer and Information Technology), 6(2), 118–127.
Damanik, M. R. S., & Restu, R. (2012). Pemetaan tingkat risiko banjir dan longsor sumatera utara berbasis sistem informasi geografis. Jurnal Geografi, 4(1), 29–42.
Furqon, M. T., & Muflikhah, L. (2016). Clustering the potential risk of tsunami using density-based spatial clustering of application with noise (dbscan). Journal of Enviromental Engineering and Sustainable Technology, 3(1), 1–8. https://doi.org/10.21776/ub.jeest.2016.003.01.1
Harahap, M. E., Lufti, M., & Muthalib, A. (2015). Pengaruh pengetahuan dan sikap terhadap kesiapsiagaan masyarakat dalam menghadapi banjir di desa perkebunan bukit lawang kecamatan bahorok tahun 2011. Jurnal Ilmiah Keperawatan Imelda, 1(1), 22–31.
Harjanto, T. D., Vatresia, A., & Faurina, R. (2021). Analisis penetapan skala prioritas penanganan balita stunting menggunakan metode dbscan clustering (studi kasus data dinas kesehatan kabupaten lebong). Rekursif: Jurnal Informatika, 9(1). https://doi.org/10.33369/rekursif.v9i1.14982
Hatta, M. S., Azmi, F., & Setianingsih, C. (2021). Clustering pada data sentimen penggunaan transportasi online menggunakan algoritma spectral clustering. E-Proceedings of Engineering, 8(6).
Hermanto, T. I., & Sunandar, M. A. (2020). Analisis data sebaran penyakit menggunakan algoritma density based spatial clustering of applications with noise. Jurnal Sains Komputer Dan Teknologi Informasi, 3(1). https://doi.org/10.33084/jsakti.v3i1.1775
Isnarwaty, D. P., & Irhamah, I. (2020). Text clustering pada akun twitter layanan ekspedisi jne, j&t, dan pos indonesia menggunakan metode density-based spatial clustering of applications with noise (dbscan) dan k-means. Jurnal Sains Dan Seni ITS, 8(2). https://doi.org/10.12962/j23373520.v8i2.49094
Jatipaningrum, M. T., Azhari, S. E., & Suryowati, K. (2022). Pengelompokan kabupaten dan kota di provinsi jawa timur berdasarkan tingkat kesejahteraan dengan metode k-means dan density-based spatial clustering of applications with noise. Jurnal Derivat: Jurnal Matematika Dan Pendidikan Matematika, 9(1), 70–81. https://doi.org/10.31316/j.derivat.v9i1.2832
Kristianto, A. (2021). Analisa performa k-means dan dbscan dalam clustering minat penggunaan transportasi umum. Elkom : Jurnal Elektronika Dan Komputer, 14(2), 368–372. https://doi.org/10.51903/elkom.v14i2.551
Mahendra, R., Azmi, F., & Setianingsih, C. (2021). Klasterisasi pada data penggunaan listrik di gedung telkom university menggunakan algoritma density-based spatial clustering of application with noise (dbscan). E-Proceedings of Engineering, 8(6).
Nurhaliza, N., & Mustakim, M. (2021). Pengelompokan data kasus covid-19 di dunia menggunakan algoritma dbscan. IJIRSE: Indonesian Journal of Informatic Research and Software Engineering, 1(1), 1–8.
Rahman, R. R. A., & Wijayanto, A. W. (2021). Pengelompokan data gempa bumi menggunakan algoritma dbscan. Jurnal Meteorologi Dan Geofisika, 22(1). https://doi.org/10.31172/jmg.v22i1.738
Rohalidyawati, W., Rahmawati, R., & Mustafid, M. (2020). Segmentasi pelanggan e-money dengan menggunakan algoritma dbscan (density based spatial clustering applications with noise) di provinsi dki jakarta. Jurnal Gaussian, 9(2), 162–169. https://doi.org/10.14710/j.gauss.v9i2.27818
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017a). Dbscan revisited, revisited: Why and how you should (still) use dbscan. ACM Transactions on Database Systems, 42(3), 1–21. https://doi.org/10.1145/3068335
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017b). Why and How You Should (Still) Use DBSCAN. ACM Transactions on Database Systems, 42(3), 1–21.
Septian, A., Elvarani, A. Y., Putri, A. S., Maulia, I., Damayanti, L., Pahlevi, M. Z., & Aswad, F. H. (2020). Identifikasi zona potensi banjir berbasis sistem informasi geografis menggunakan metode overlay dengan scoring di kabupaten agam, sumatera barat. Jurnal Geosains Dan Remote Sensing, 1(1). https://doi.org/10.23960/jgrs.2020.v1i1.25
Setiawan, Y., Purwandari, E. P., Wijanarko, A., & Sunandi, E. (2020). Pemetaan zonasi rawan banjir dengan analisis indeks rawan banjir menggunakan metode fuzzy simple adaptive weighting. Pseudocode, 7(1), 78–87. https://doi.org/10.33369/pseudocode.7.1.78-87
Sitorus, I. H. O., Bioresita, F., & Hayati, N. (2021). Analisa tingkat rawan banjir di daerah kabupaten bandung menggunakan metode pembobotan dan scoring. Jurnal Teknik ITS, 10(1). https://doi.org/10.12962/j23373539.v10i1.60082
Sitorus, S. B. A. (2022). Peran badan penanggulangan bencana daerah dalam penanggulangan banjir di kota tebing tinggi provinsi sumatera utara. Institut Pemerintahan Dalam Negeri.
Sukmayadi, C., Primajaya, A., & Maulana, I. (2021). Penerapan algoritma k-medoids dalam menentukan daerah rawan banjir di kabupaten karawang. INFORMAL: Informatics Journal, 6(3), 187. https://doi.org/10.19184/isj.v6i3.25423
Tampubolon, K. (2018). Aplikasi sistem informasi geografis (sig) sebagai penentuan kawasan rawan banjir di kota medan.
Tasia, E., & Afdal, M. (2023). Perbandingan algoritma k-means dan k-medoids untuk clustering daerah rawan banjir di kabupaten rokan hilir. Indonesian Journal of Informatic Research and Software Engineering (IJIRSE), 3(1), 65–73. https://doi.org/10.57152/ijirse.v3i1.523
UU No. 24. (2007). Undang-undang (uu) nomor 24 tahun 2007 tentang penanggulangan bencana.
Wijaya, D. V., Alfiansyah, Y. F., Junior, A. R., Masruriyah, A. F. N., Indra, J., Hikmayanti, H., & Siregar, A. M. (2021). Analisis sentimen pada buletin menggunakan algoritme dbscan. Conference on Innovation and Application of Science and Technology (CIASTECH).
DOI: http://dx.doi.org/10.24042/djm.v6i2.19043
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Desimal: Jurnal Matematika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Desimal: Jurnal Matematika is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
