{"title":"Clustering Reservoirs in Indonesia Based on Area and Volume Using the K-Means Algorithm","authors":"Shofinurdin Shofinurdin, Afifah Khaerani, Arie Wibowo","doi":"10.24114/cess.v8i2.44390","DOIUrl":null,"url":null,"abstract":"Embung adalah cekungan yang digunakan untuk mengatur dan menampung aliran hujan dan meningkatkan kualitas air di badan air yang terkait. Embung juga digunakan untuk menjaga kualitas air tanah, mencegah banjir, menjaga estetika, dan mengairi. Terdapat 1.446 embung yang tersebar di seluruh wilayah Indonesia yang mempunyai luas dan volume yang beragam. Penelitian ini bertujuan untuk mengelompokkan embung di Indonesia berdasarkan luas dan volume menggunakan metode data mining klastering dengan algoritma K-Means. Sebelumnya, pengklasteran embung hanya dilakukan berdasarkan: tujuan pembangunan, penggunaan, aliran air, dan bahan pembentuknya. Hasil penelitian menunjukkan bahwa pengelompokkan embung yang terbaik adalah menjadi 4 klaster dengan rincian klaster kecil: 1.414 embung, klaster sedang: 24 embung, klaster tinggi: 7 embung dan klaster sangat tinggi: 1 embung.An embung (Reservoir) is a basin used to control and collect rainfall runoff and improve the water quality in the associated water bodies. Embungs are utilized for preserving groundwater quality, preventing floods, enhancing aesthetics, and facilitating irrigation. There are 1,446 embungs scattered across the entire territory of Indonesia, varying in size and volume. This study aims to cluster the embungs in Indonesia based on their size and volume using the clustering data mining technique with the K-Means algorithm. Previously, embung clustering was only performed based on their purpose of construction, usage, water flow, and constituent materials. The research findings indicate that the optimal clustering of embungs consists of four clusters, with the following details: small cluster (1,414 embungs), medium cluster (24 embungs), high cluster (7 embungs), and very high cluster (1 embung). This research will prove beneficial in the fields of data mining and hydrology. ","PeriodicalId":53361,"journal":{"name":"CESS Journal of Computer Engineering System and Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CESS Journal of Computer Engineering System and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24114/cess.v8i2.44390","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Embung adalah cekungan yang digunakan untuk mengatur dan menampung aliran hujan dan meningkatkan kualitas air di badan air yang terkait. Embung juga digunakan untuk menjaga kualitas air tanah, mencegah banjir, menjaga estetika, dan mengairi. Terdapat 1.446 embung yang tersebar di seluruh wilayah Indonesia yang mempunyai luas dan volume yang beragam. Penelitian ini bertujuan untuk mengelompokkan embung di Indonesia berdasarkan luas dan volume menggunakan metode data mining klastering dengan algoritma K-Means. Sebelumnya, pengklasteran embung hanya dilakukan berdasarkan: tujuan pembangunan, penggunaan, aliran air, dan bahan pembentuknya. Hasil penelitian menunjukkan bahwa pengelompokkan embung yang terbaik adalah menjadi 4 klaster dengan rincian klaster kecil: 1.414 embung, klaster sedang: 24 embung, klaster tinggi: 7 embung dan klaster sangat tinggi: 1 embung.An embung (Reservoir) is a basin used to control and collect rainfall runoff and improve the water quality in the associated water bodies. Embungs are utilized for preserving groundwater quality, preventing floods, enhancing aesthetics, and facilitating irrigation. There are 1,446 embungs scattered across the entire territory of Indonesia, varying in size and volume. This study aims to cluster the embungs in Indonesia based on their size and volume using the clustering data mining technique with the K-Means algorithm. Previously, embung clustering was only performed based on their purpose of construction, usage, water flow, and constituent materials. The research findings indicate that the optimal clustering of embungs consists of four clusters, with the following details: small cluster (1,414 embungs), medium cluster (24 embungs), high cluster (7 embungs), and very high cluster (1 embung). This research will prove beneficial in the fields of data mining and hydrology.