K-Means Algorithm’s Implementation to Facilitate Grouping of Landslide-Prone Areas

Maria Ulfah; Andi Sri Irtawaty; Zulkifli; Subur Mulyanto

doi:10.2991/978-2-38476-232-3_11

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K-Means Algorithm’s Implementation to Facilitate Grouping of Landslide-Prone Areas

Authors

Maria Ulfah¹^{, *}, Andi Sri Irtawaty¹, Zulkifli², Subur Mulyanto²

¹Electrical Engineering Departement, Politeknik Negeri Balikpapan, Balikpapan, Indonesia

²Mechanical Engineering Departement, Politeknik Negeri Balikpapan, Balikpapan, Indonesia

^*Corresponding author. Email: maria.ulfah@poltekba.ac.id

Corresponding Author

Maria Ulfah

Available Online 14 May 2024.

DOI: 10.2991/978-2-38476-232-3_11 How to use a DOI?
Keywords: K-Means Algorithm; Landslide-Prone Areas; Data Mining; Clustering Area
Abstract: Indonesia is one of the countries that has hydrometeorological disaster vulnerability, one of which is Balikpapan City. Natural disasters have the potential to damage the environment, harm property, and cause casualties. Landslide disaster mitigation is still not optimal and there is no grouping of landslide-prone areas in Balikpapan. Therefore, it is necessary to research to produce a grouping of landslide vulnerability levels in 34 sub-districts. To group vulnerable areas, the clustering method with the K-Means algorithm was used from 34 data on the impact of landslides from each sub-district in Balikpapan with Rapid Miner tool. Clusters or groupings formed: not vulnerable, vulnerable, and very vulnerable. The very vulnerable cluster contains one sub-district, namely Sepinggan, the vulnerable cluster consists of 4 sub-districts which are Sungai Nangka, Baru Tengah, Manggar, and Baru Ilir. The non-vulnerable cluster consists of the rest of the 29 sub-districts with a David Bouldin performance value of 0.009.
Copyright: © 2024 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 10th International Conference on Technical and Vocational Education and Training (ICTVET 2023)
Series: Advances in Social Science, Education and Humanities Research
Publication Date: 14 May 2024
ISBN: 10.2991/978-2-38476-232-3_11
ISSN: 2352-5398
DOI: 10.2991/978-2-38476-232-3_11 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Maria Ulfah
AU  - Andi Sri Irtawaty
AU  - Zulkifli
AU  - Subur Mulyanto
PY  - 2024
DA  - 2024/05/14
TI  - K-Means Algorithm’s Implementation to Facilitate Grouping of Landslide-Prone Areas
BT  - Proceedings of the 10th International Conference on Technical and Vocational Education and Training (ICTVET 2023)
PB  - Atlantis Press
SP  - 80
EP  - 85
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-232-3_11
DO  - 10.2991/978-2-38476-232-3_11
ID  - Ulfah2024
ER  -

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