Klasterisasi Kerawanan Banjir Dengan Algoritma K-Means dan Fuzzy C-Means (Studi Kasus : Daerah Penyangga IKN - Kabupaten Kutai Kartanegara)
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Institusion
Institut Teknologi Kalimantan
Author
Nurjanah, Desi
Subject
QA75 Electronic computers. Computer science
Datestamp
2023-07-20 08:10:47
Abstract :
Flooding is a situation where areas that are not usually inundated, such as agricultural land, settlements, and downtown areas, become inundated due to water. Floods can also occur when the flow of water in rivers or drainage channels exceeds its normal capacity. This study describes the K-Means and Fuzzy C-Means Algorithm methods for clustering flood-prone areas based on Districts in Kutai Kartanegara Regency. This research began with data collection in the form of rainfall, land elevation, the number of victims affected, the number of damaged houses, the amount of damage to facilities (schools, places of worship and health facilities) and the number of flood events. Before the data is processed using these two methods, data normalization will be carried out in a dataset which aims to shape the data into positional values from the same range. K-Means and Fuzzy C-Means are used to identify groups in each sub-district in Kutai Kartanegara Regency that have a level of vulnerability to floods. At this stage, 3 initial clusters were carried out, namely high-level vulnerability clusters, medium-level vulnerability clusters, and low-level vulnerability clusters. The validity test produces a Silhouette Index value of 0.574283589 and a Partition Coefficient Index of 0.78905. The results of the K-Means method with the standard deviation within and between clusters are equal to and the Fuzzy C-Means method for the standard deviation within and between clusters is 0.3489. based on the value of the silhouette index, partition coefficient index and standard deviation within and between clusters it results that Fuzzy C-Means is the best method of this study.
Flooding is a situation where areas that are not usually inundated, such as agricultural land, settlements, and downtown areas, become inundated due to water. Floods can also occur when the flow of water in rivers or drainage channels exceeds its normal capacity. This study describes the K-Means and Fuzzy C-Means Algorithm methods for clustering flood-prone areas based on Districts in Kutai Kartanegara Regency. This research began with data collection in the form of rainfall, land elevation, the number of victims affected, the number of damaged houses, the amount of damage to facilities (schools, places of worship and health facilities) and the number of flood events. Before the data is processed using these two methods, data normalization will be carried out in a dataset which aims to shape the data into positional values from the same range. K-Means and Fuzzy C-Means are used to identify groups in each sub-district in Kutai Kartanegara Regency that have a level of vulnerability to floods. At this stage, 3 initial clusters were carried out, namely high-level vulnerability clusters, medium-level vulnerability clusters, and low-level vulnerability clusters. The validity test produces a Silhouette Index value of 0.574283589 and a Partition Coefficient Index of 0.78905. The results of the K-Means method with the standard deviation within and between clusters are equal to and the Fuzzy C-Means method for the standard deviation within and between clusters is 0.3489. based on the value of the silhouette index, partition coefficient index and standard deviation within and between clusters it results that Fuzzy C-Means is the best method of this study.
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02191005_Cover.pdf
02191005_statement_of_aunthenticity.pdf
02191005_notations.pdf
02191005_chapter_1.pdf
02191005_chapter_2.pdf
02191005_chapter_3.pdf
02191005_chapter_4.pdf
02191005_conclusion.pdf
02191005_bibliography.pdf
02191005_enclosure.pdf
02191005_presentation.pdf
02191005_form020.pdf
02191005_publishing_agreement.pdf
02191005_approval_sheet.pdf
02191005_preface.pdf
02191005_abstract_id.pdf
02191005_abstract_en.pdf
02191005_table_of_content.pdf
02191005_ilustrations.pdf
02191005_tables.pdf
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