Pengelompokan Wilayah Bencana Banjir di Indonesia Menggunakan Algoritma K-Means


Authors

  • Wenny Tarisa Oktaviany Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Fitri Insani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Alwis Nazir Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Pizaini Pizaini Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia

DOI:

https://doi.org/10.47065/bulletincsr.v5i4.608

Keywords:

Flood; Indonesia; K-Means; Sillhouette Coefficient

Abstract

Floods are one of the natural disasters that often occur in Indonesia, especially during the rainy season. This disaster is caused by various factors, both natural and caused by human activities, such as high rainfall, poor drainage systems, land conversion, and suboptimal spatial planning. The impact of floods is very detrimental, both physically and psychologically, including loss of life and damage to property. Therefore, a method is needed to group areas based on their level of vulnerability to flooding. This study aims to group flood disaster areas in Indonesia using the K-Means algorithm. The data used comes from the BNPB Geoportal covering flood events from January 2020 to December 2024, with a total of 7,487 events from 498 areas. Based on the test results obtained using the Silhouette Coefficient, it shows that 2 clusters were selected as the best number of clusters with a Silhouette Coefficient value of 0.8461 which is included in the strong clustering structure. Of the 2 clusters obtained, cluster 1 is a high-risk category consisting of 35 areas, while cluster 2 is a low-risk category consisting of 463 areas. The results of this study can provide information for related parties to improve the efficiency of flood disaster management.

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References

S. Ulya, H. Hapidin, and Z. Akbar, “SIGANA Banjir: Game Edukasi Kesiapsiagaan Bencana Banjir Untuk Anak Usia 5-6 Tahun,” Murhum J. Pendidik. Anak Usia Dini, vol. 4, no. 2, pp. 151–164, Aug. 2023, doi: 10.37985/murhum.v4i2.311.

M. Ridwan, A. Zainuddin, M. Kasim, M. Yahya, P. Studi Destinasi Pariwisata Politeknik Pariwisata Makassar Jalan Gunung Rinjani, and S. Selatan, “PEMETAAN DAERAH BENCANA PADA DESTINASI KOTA PALOPO (STUDI KASUS BENCANA BANJIR DAN LONGSOR) (Disaster Space Mapping in Palopo City Destinations (Case Study of Flood and Landslide Disaster),” 2022.

D. I. Ramadhani, O. Damayanti, O. Thaushiyah, and A. R. Kadafi, “Penerapan Metode K-Means Untuk Clustering Desa Rawan Bencana Berdasarkan Data Kejadian Terjadinya Bencana Alam,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 3, p. 749, Jun. 2022, doi: 10.30865/jurikom.v9i3.4326.

N. Amaliyah Wibowo and A. Maman Abadi, “ANALISIS TINGKAT KERAWANAN BENCANA ALAM BANJIR DI KABUPATEN PURBALINGGA DENGAN FUZZY LOGIC ANALYSIS OF FLOOD VULNERABILITY LEVEL IN PURBALINGGA REGENCY WITH FUZZY LOGIC,” 2022.

M. D. H. Rahiem and F. Widiastuti, “Pembelajaran Mitigasi Bencana Alam Gempa Bumi untuk Anak Usia Dini melalui Buku Bacaan Bergambar,” J. Obs. J. Pendidik. Anak Usia Dini, vol. 5, no. 1, p. 36, Apr. 2020, doi: 10.31004/obsesi.v5i1.519.

E. Kurniati, V. Adriany, M. Mirawati, R. M. El-Seira, and I. Winangsih, “Identifikasi Kesiapsiagaan Guru PAUD sebagai Upaya Pengurangan Risiko Bencana Banjir di Bandung,” J. Obs. J. Pendidik. Anak Usia Dini, vol. 4, no. 2, p. 840, Feb. 2020, doi: 10.31004/obsesi.v4i2.388.

W. Wirmando, F. Patarru’, and J. L. Saranga’, “MENINGKATKAN PENGETAHUAN DAN KESIAPSIAGAAN MASYARAKAT DALAM MENGHADAPI BENCANA BANJIR MELALUI EDUKASI DAN SIMULASI MENGGUNAKAN TABLETOP DISASSTER EXERCISE,” JMM (Jurnal Masy. Mandiri), vol. 6, no. 3, p. 2166, Jun. 2022, doi: 10.31764/jmm.v6i3.8244.

A. Irfan Abdurrahman, B. Yuwono, and Y. Fauziah, “PENERAPAN METODE MULTI ATTRIBUTE UTILITY THEORY (MAUT) DALAM PEMETAAN TINGKAT DAMPAK BENCANA BANJIR DI KABUPATEN BANTUL,” 2020.

H. Setiawan et al., “ANALISIS PENYEBAB BANJIR DI KOTA SAMARINDA,” 2020. [Online]. Available: https://ejournal.upi.edu/index.php/gea

B. Banjir, P. dan Pengendalian Pemanfaatan Ruang, P. U. dan Pengendalian Pemanfaatan Ruang Berdasarkan Penataan Ruang dan RUU Cipta Kerja, S. Nurhayati Qodriyatun, and P. R. Penelitian Badan Keahlian DPR Jl Gatot Subroto, “Sri Nurhayati Qodriyatun”, doi: 10.22212/aspirasi.v11i1.1590.

N. Agusdianita and V. Karjiyati, “STUDI DESKRIPTIF SIKAP KESIAPSIAGAAN BANJIR ANAK SD DITINJAU DARI SEGI ETNIS DI DAS KOTA BENGKULU.” [Online]. Available: https://www.jurnalfai-uikabogor.org/attadib

F. A. Setyorini, “Menakar Paradigma Penanggulangan Bencana Melalui Analisis Undang-Undang No. 24 Tahun 2007 Tentang Penanggulangan Bencana,” J. Soc. Polit. Gov., vol. 5, no. 2, pp. 97–113, 2023, doi: 10.24076/jspg.v5i2.1376.

R. A. Indraputra and R. Fitriana, “K-Means Clustering Data COVID-19”.

W. Utomo, “The comparison of k-means and k-medoids algorithms for clustering the spread of the covid-19 outbreak in Indonesia,” Ilk. J. Ilm., vol. 13, no. 1, pp. 31–35, Apr. 2021, doi: 10.33096/ilkom.v13i1.763.31-35.

T. Hardiani, “Analisis Clustering Kasus Covid 19 di Indonesia Menggunakan Algoritma K-Means,” J. Nas. Pendidik. Tek. Inform., vol. 11, no. 2, pp. 156–165, Aug. 2022, doi: 10.23887/janapati.v11i2.45376.

A. Supriyadi et al., “PERBANDINGAN ALGORITMA K-MEANS DENGAN K-MEDOIDS PADA PENGELOMPOKAN ARMADA KENDARAAN TRUK BERDASARKAN PRODUKTIVITAS.”

D. D. Aulia, “COMPARISON PERFORMANCE OF K-MEDOIDS AND K-MEANS ALGORITHMS IN CLUSTERING COMMUNITY EDUCATION LEVELS Jurnal Nasional Pendidikan Teknik Informatika?: JANAPATI | 274,” vol. 12, no. 2, pp. 273–282, 2023.

M. M. Effendi and A. Siswandi, “Analysis Prediksi Wilayah Rawan Banjir dengan Algoritma K-Means,” vol. 5, no. 2, pp. 697–703, 2024, doi: 10.47065/josh.v5i2.4770.

A. Ahyuna, M. Lasena, R. Aminuddin, and Z. Azhar, “Pembentukan Pola Peminjaman Buku Pada Perpustakaan Dengan Menerapkan Metode CART dan Normalisasi Z-Score,” Technol. Sci., vol. 6, no. 1, 2024, doi: 10.47065/bits.v6i1.5238.

S. E. Saqila, I. P. Ferina, and A. Iskandar, “Analisis Perbandingan Kinerja Clustering Data Mining Untuk Normalisasi Dataset,” J. Sist. Komput. dan Inform., vol. 5, no. 2, p. 356, Dec. 2023, doi: 10.30865/json.v5i2.6919.

F. P. Ferdy Pangestu, N. Y. Nur Yasin, R. C. Ronald Chistover Hasugian, and Y. Yunita, “Penerapan Algoritma K-Means Untuk Mengklasifikasi Data Obat,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 12, no. 1, pp. 53–62, Mar. 2023, doi: 10.32736/sisfokom.v12i1.1461.

S. A. D. Darmawan and Karmilasari, “PENERAPAN METODE K-MEANS CLUSTERING DAN SIMPLE MOVING AVERAGE UNTUK MEMPREDIKSI JENIS PENYAKIT DI PROVINSI JAWA TIMUR,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 4, pp. 877–886, Aug. 2024, doi: 10.25126/jtiik.1148703.

S. Kasus, : Hoyweapstore, D. Triyansyah1, and D. Fitrianah2, “Analisis Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing”, doi: 10.22441/incomtech.v8i2.4174.

S. Multi Fani and R. Santoso, “PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING,” vol. 10, pp. 583–593, [Online]. Available: https://ejournal3.undip.ac.id/index.php/gaussian/

N. Nugroho and F. D. Adhinata, “Penggunaan Metode K-Means dan K-Means++ Sebagai Clustering Data Covid-19 di Pulau Jawa,” Teknika, vol. 11, no. 3, pp. 170–179, Oct. 2022, doi: 10.34148/teknika.v11i3.502.


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Published: 2025-06-26

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How to Cite

Wenny Tarisa Oktaviany, Fitri Insani, Alwis Nazir, & Pizaini, P. (2025). Pengelompokan Wilayah Bencana Banjir di Indonesia Menggunakan Algoritma K-Means. Bulletin of Computer Science Research, 5(4), 542-553. https://doi.org/10.47065/bulletincsr.v5i4.608

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