UMN Knowledge Center

Cluster-based water level patterns detection

Ferdinand, Friska Natalia and Soelistio, Yustinus Eko and Ferdinand, Ferry Vincenttius and Murwantara, I Made (2019) Cluster-based water level patterns detection. TELKOMNIKA (Telecommunication, Computing, Electronics and Control), 17 (3). pp. 1376-1384. ISSN 2302-9293

Full text not available from this repository.
Official URL:


Indonesian Disaster Data and Information in 2016 showed that flood has reached a soaring 32.2% overall. In one of the common flood region (2016), Tangerang, the flood had impacted 30,949, and destroys more than 400 residentials. In spite of this dreadful fact, Tangerang has no systematically ways of detecting the flood patterns. Therefore, there is urgency for a system that is able to detect potential flood risks in Tangerang. This study explores a mean to systematically find flood patterns in Tangerang and attempt to visualize the risks based on 11 years of data on four major river stations within Tangerang vicinity. All the data obtained from Ciliwung Cisadane River Basin Center (BBWS) between 2009 until 2017 with total data of 368,184 rows. This study proposes an interactive dashboard based on the water level data covering rivers of Angke, Pesanggrahan, and Cisadane. Three clustering methods are implemented, the K-Medoids, DBScan, and x-means, to segregate the water level data, taken from four stations obtained from Ciliwung Cisadane River Basin Center (BBWS), into meaningfull periodic flood patterns. The output of this research is an interactive dashboard created based on the newly found patterns. The dashboard is designed to be simple and easy to use for non-technical persons. We believe that the output of this research could be implemented into the decision-making process taken by the Ciliwung Cisadane River Basin Center (BBWS) in order to improve countermeasure attempts on the potentially flooded areas.

Item Type: Article
Uncontrolled Keywords: dashboard; DBscan; K-medoids; knowledge discovery in databases; X-means;
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 001 Knowledge
000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming
500 Science and Mathematic > 510 Mathematics > 510 Mathematics
Divisions: Fakultas Teknik Informatika > Program Studi Sistem Informasi
Depositing User: mr admin umn
Date Deposited: 24 Nov 2021 13:35
Last Modified: 12 Jan 2022 06:53

Actions (login required)

View Item View Item