research-article

D-Loc Apps: A Location Detection Application Based on Social Media Platform in the Event of A Flood Disaster

Authors:

Devi Fitrianah,

Dwiki Jatikusumo,

Ida NurhaidaAuthors Info & Claims

APIT '20: Proceedings of the 2020 2nd Asia Pacific Information Technology Conference

Pages 41 - 45

https://doi.org/10.1145/3379310.3381041

Published: 29 March 2020 Publication History

Abstract

The purpose of this research is to develop a disaster location detector application, specifically about flood that happens in the area of Jakarta, Indonesia. According to social media data updated by the users of twitter, search inquiry is done by its users with the keyword 'seputaran banjir' (in Bahasa Indonesia) which is combined with the Location Based Service function of the determined word search. The method in creating this application is Extreme Programming that uses repetitive and incremental development process approach. By using twitter status data about Jakarta's area, this application has been successfully developed and the result can be shown in the form of twitter status updates from its users that contain words about flood. The search results of flood location validation are coordinates, which are done by checking the availability of the geospatial information from each update of the data. The average accuracy of the search result is 83.7% out of all the location search results.

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Cited By

Humaira NSamadi VHubig N(2023)DX-FloodLine: End-To-End Deep Explainable Pipeline for Real Time Flood Scene Object Detection From Multimedia ImagesIEEE Access10.1109/ACCESS.2023.332131211(110644-110655)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3321312
Kamoji SKalla M(2023)Effective Flood prediction model based on Twitter Text and Image analysis using BMLP and SDAE-HHNNEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106365123(106365)Online publication date: Aug-2023
https://doi.org/10.1016/j.engappai.2023.106365
Anta VLiestyo IWarnars H(2021)Mobile Application for flood disaster in Jakarta2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)10.1109/ICAIS50930.2021.9395799(506-510)Online publication date: 25-Mar-2021
https://doi.org/10.1109/ICAIS50930.2021.9395799

Index Terms

D-Loc Apps: A Location Detection Application Based on Social Media Platform in the Event of A Flood Disaster
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms

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cover image ACM Other conferences

APIT '20: Proceedings of the 2020 2nd Asia Pacific Information Technology Conference

January 2020

185 pages

ISBN:9781450376853

DOI:10.1145/3379310

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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New York, NY, United States

Publication History

Published: 29 March 2020

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APIT 2020: 2020 2nd Asia Pacific Information Technology Conference

January 17 - 19, 2020

Bali Island, Indonesia

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Cited By

Humaira NSamadi VHubig N(2023)DX-FloodLine: End-To-End Deep Explainable Pipeline for Real Time Flood Scene Object Detection From Multimedia ImagesIEEE Access10.1109/ACCESS.2023.332131211(110644-110655)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3321312
Kamoji SKalla M(2023)Effective Flood prediction model based on Twitter Text and Image analysis using BMLP and SDAE-HHNNEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106365123(106365)Online publication date: Aug-2023
https://doi.org/10.1016/j.engappai.2023.106365
Anta VLiestyo IWarnars H(2021)Mobile Application for flood disaster in Jakarta2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)10.1109/ICAIS50930.2021.9395799(506-510)Online publication date: 25-Mar-2021
https://doi.org/10.1109/ICAIS50930.2021.9395799

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