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All Journal Algoritma: Jurnal Matematika, Ilmu Pengetahuan Alam, Kebumian dan Angkasa
Febby Arisca Zurfani
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Earth & Planetary Sciences Mathematics

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Analisis Metode Clustering K-Means pada Zonasi Daerah Terdampak Banjir di Kota Medan dengan Evaluasi Silhouette Coefficient Febby Arisca Zurfani; Sawaluddin; Mardiningsih; Muhammad Romi Syahputra
Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa Vol. 2 No. 6 (2024): Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/algoritma.v2i6.270

Abstract

Clustering is one of the fields of study that discusses data. Clustering is used to find and group data based on its traits or characteristics. Clustering can also be used for class-identified data. However, the clustering method automatically clusters the data before the class identifier is known. Based on the data obtained, the city of Medan, which has a population of approximately 2,460,858 people and an area of 26,510 hectares or 3.6% of the total area of North Sumatra Province, is classified as Flood-prone (BPS). Floods that occur almost 10 to 12 times a year in Medan City are influenced by the condition of the downstream Deli and Belawan rivers. Based on the results of the k-means clustering that has been carried out, the areas that are safe from flooding are the districts of Meddan Amplas, Medan Denai, Medan Area, Medan Kota, Medan Petisah, Medan Perjuangan, Medan Tembung, Medan Deli, and Medan Labuhan. Areas prone to flooding are Medan Tuntungan, Medan Sunggal, Medan Helvetia, West Medan, and Medan Marelan. Meanwhile, the areas most prone to flooding are Medan Johor, Medan Maimun, Medan Polonia, Medan Baru, Medan Selayang, Medan Timur, and Medan Belawan based on the evaluation of the accuracy of the silhouette method of 0.9 and can be declared significant.
Co-Authors Mardiningsih Sawaluddin Syahputra, Muhammad Romi