Authors:

I Made Oka Widyantara, I Made Dwi Asana Putra, Ida Bagus Putu Adnyana

Abstract:

“This paper intends to explain the development of Coastal Video Monitoring System (CoViMoS) with the main characteristics including low-cost and easy implementation. CoViMoS characteristics have been realized using the device IP camera for video image acquisition, and development of software applications with the main features including detection of shoreline and it changes are automatically. This capability was based on segmentation and classification techniques based on data mining. Detection of shoreline is done by segmenting a video image of the beach, to get a cluster of objects, namely land, sea and sky, using Self Organizing Map (SOM) algorithms. The mechanism of classification is done using K-Nearest Neighbor (K-NN) algorithms to provide the class labels to objects that have been generated on the segmentation process. Furthermore, the classification of land used as a reference object in the detection of costline. Implementation CoViMoS system for monitoring systems in Cucukan Beach, Gianyar regency, have shown that the developed system is able to detect the shoreline and its changes automatically.”

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PDF:

https://jurnal.harianregional.com/jeei/full-32228

Published

2017-02-02

How To Cite

WIDYANTARA, I Made Oka; PUTRA, I Made Dwi Asana; ADNYANA, Ida Bagus Putu. COVIMOS: A Coastal Video Monitoring System.Journal of Electrical, Electronics and Informatics, [S.l.], v. 1, n. 1, p. 1-6, feb. 2017. ISSN 2622-0393. Available at: https://ojs.unud.ac.id/index.php/JEEI/article/view/32228. Date accessed: 28 Aug. 2025. doi:https://doi.org/10.24843/JEEI.2017.v01.i01.p01.

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ABNT, APA, BibTeX, CBE, EndNote - EndNote format (Macintosh & Windows), MLA, ProCite - RIS format (Macintosh & Windows), RefWorks, Reference Manager - RIS format (Windows only), Turabian

Issue

Vol 1 No 1 (2017): JEEI (February 2017)

Section

Articles

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License