Vol.9, No.3, August 2020.                                                                                                                                                                                ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Application of Density Based Clustering of Disaster Location in Realtime Social Media


Mochammad Haldi Widianto, Ivan Diryana Sudirman, Muhammad Hanif Awaluddin


© 2020 Mochammad Haldi Widianto, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 9, Issue 3, Pages 929-936, ISSN 2217-8309, DOI: 10.18421/TEM93-13, August 2020.


Received: 24 December 2019.

Revised:   09 June 2020.
Accepted: 16 June 2020.
Published: 28 August 2020.




Online life is used as a method of finding information, one of which is Twitter as the medium. The occurrence of natural disasters is very detrimental. Therefore, the application is needed to see natural disasters through social media Twitter. A small number of studies using clustering methods based on Twitter user data density are the beginning of this research. With the availability of data in certain areas makes it easy to group. After that, the data is grouped based on a high degree of similarity. One result of applying this method is the location of the disaster. NER-based rules are used to discover out the area of the disaster. Data accuracy testing is performed using the Silhouette coefficient.


Keywords –Density-based clustering, NER rulebased,location disaster, Silhouette coefficient.



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