SETAPATI, GERRY (2021) IMPLEMENTATION OF DATA MINING FOR CLUSTERING DISASTERPRONE AREAS IN SOUTH SUMATRA USING K-MEANS ALGORITHM. Undergraduate Thesis thesis, UIN RADEN FATAH PALEMBANG.
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Abstract
Indonesia is a country that has a natural structure consisting of the meeting of tectonic plates. This has resulted in many regions in Indonesia being very vulnerable to natural disasters, one of which is the Province of South Sumatra. This study aims to group disaster-prone areas in South Sumatra by utilizing the data mining process using the Clustering technique. The algorithm used for cluster formation is the K-Means algorithm. K-Means is one of the non-hierarchical clustering data methods that can group disaster data into several clusters based on the similarity of the data, so that disaster data with the same characteristics are grouped in one cluster and those with different characteristics are grouped in another cluster. The results of this study are groups of disaster-prone areas based on three categories, namely low disaster-prone areas, moderate disaster-prone areas and high disaster-prone areas. This research is expected to be additional information for the government in the process of disaster management in South Sumatra. Keywords: Natural Disaster, Data Mining, K-Means, Clustering, Disaster Prone
| Item Type: | Thesis (Undergraduate Thesis) |
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| Uncontrolled Keywords: | Natural Disaster, Data Mining, K-Means, Clustering, Disaster Prone |
| Subjects: | Sains dan Teknologi > Sistem Informasi |
| Divisions: | Fakultas Sains dan Teknologi > 57201 - Sistem Informasi |
| Depositing User: | UPT PERPUSTAKAAN #3 |
| Date Deposited: | 28 Apr 2022 02:56 |
| Last Modified: | 28 Apr 2022 02:56 |
| URI: | http://repository.radenfatah.ac.id/id/eprint/20391 |
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