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All Journal Seminar Nasional Teknologi Informasi Komunikasi dan Industri Applied Information System and Management Jurnal Informasi dan Komputer TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research
Alwis Nazir
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Journal : Bulletin of Computer Science Research

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Pengelompokan Wilayah Bencana Banjir di Indonesia Menggunakan Algoritma K-Means Wenny Tarisa Oktaviany; Fitri Insani; Alwis Nazir; Pizaini
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.608

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.
Co-Authors Aminuyati Elin Haerani Elvia Budianita Fadhilah Syafria Fitri Insani Iis Afrianty Iwan Iskandar Jasril Jasril Lia Anggraini M Fikry Muhammad Alvin Muhammad Fikry Muhammad Habib Nazlis Muhammad Hasbi Assidiqqi Muhammad Ichsanul Bukhari Nailatul Fadhilah Nuradha Liza Utami Pizaini Putri Mardatillah Rahmad Kurniawan Rio Fernando Silvi Ana Sukma Evadini Teddie Darmizal Wenny Tarisa Oktaviany Yusra Pizaini