Mining Unstructured Data in Social Media for Natural Disaster Management in Indonesia

This paper proposed a model system for unstructured mining data in social media for natural disaster management in Indonesia. The model system of natural disaster management will be tested using real data where the application will be run from the stage crawl social media, tokenization, filtering, stemming, similarity measure, and Name Entity Recognizer so as to ascertain whether the software is built is in conformity with the rules of data collection events natural disasters that can be reliable. The proposed model system of natural disaster management can help the Indonesian government to calculate the impact of floods, landslides, and tornados that it could decide to focus fixes in the correct fields. If the government has made improvements by the mapping of disaster impact it will automatically proclamation of floods, landslides, and tornados in social media and news websites will decrease, and the value graph will change impacts linkages so that the government can focus subsequent repair.

[1]  H.L.H Spits Warnars,et al.  Forecasting Sebagai Decision Support Systems Aplikasi dan Penerapannya Untuk Mendukung Proses Pengambilan Keputusan. , 2018 .

[2]  Muhammad Imran,et al.  Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages , 2016, LREC.

[3]  Aron Culotta,et al.  Tweedr: Mining twitter to inform disaster response , 2014, ISCRAM.

[4]  Stefan Poslad,et al.  Exploiting hashtags for adaptive microblog crawling , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[5]  Roberto Navigli,et al.  Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity , 2013, ACL.

[6]  J. Geldermann,et al.  Social Media Text Mining and Network Analysis for Decision Support in Natural Crisis Management , 2013 .

[7]  Ted Pedersen Duluth : Measuring Degrees of Relational Similarity with the Gloss Vector Measure of Semantic Relatedness , 2012, SemEval@NAACL-HLT.

[8]  A. Culotta,et al.  A Demographic Analysis of Online Sentiment during Hurricane Irene , 2012 .

[9]  Maybin K. Muyeba,et al.  A Framework to Mine High-Level Emerging Patterns by Attribute-Oriented Induction , 2011, IDEAL.

[10]  Mohammad Ali Abbasi,et al.  TweetTracker: An Analysis Tool for Humanitarian and Disaster Relief , 2011, ICWSM.

[11]  Christopher Cheong,et al.  Social Media Data Mining: A Social Network Analysis Of Tweets During The 2010-2011 Australian Floods , 2011, PACIS.

[12]  H SpitsWarnarsH.L. Sistem Pengambilan Keputusan Penanganan Bencana Alam Gempa Bumi Di Indonesia , 2010, ArXiv.

[13]  Spits Warnars Indonesian Earthquake Decision Support System , 2010 .

[14]  Spits Warnars Rancangan Infrastruktur E-Bisnis Business Intelligence Pada Perguruan Tinggi , 2010, ArXiv.

[15]  Spits Warnars Harco Leslie Hendric ANALISA DAMPAK INVESTASI TEKNOLOGI INFORMASI PROYEK DATA WAREHOUSE PADA PERGURUAN TINGGI SWASTA DENGAN METODE SIMPLE ROI , 2010 .

[16]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[17]  H.L.H Spits Warnars,et al.  Sistem Pengambilan Keputusan Penanganan Bencana Alam Gempa Bumi Di Indonesia , 2010, 1006.1704.

[18]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[19]  Miguel Á. Carreira-Perpiñán,et al.  Multiscale conditional random fields for image labeling , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[20]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.