Natural Disaster Monitoring Information System From Social Media Data Using Naïve Bayes Algorithm
Abstract
In Indonesia, there have been several natural
disasters, such as earthquakes, tsunamis, landslides, floods, and
others. Because Indonesia is situated where the Eurasian,
Pacific, and Indo-Australian plates converge, this potential
natural disaster is caused by this location. Social media
information is expanding quickly and becoming more useful.
Social media helps to alert people of the disaster's location
during a disaster like a flood. Twitter is used as a data search
engine in this work. Twitter has been utilized effectively to
update the public on current events during emergencies. In
order to learn more, we can conduct a search using pertinent
hashtags to determining for the incident's location. The test's
results will show a map of the Indonesian region, and the
disaster's epicenter will be determined using the geolocation
provided by the tweet data. The Naive Bayes approach will be
used for classification. The clustering process occurs in real time
across every region of Indonesia. In this investigation, the
accuracy value was 75% based on the k-fold cross-validation
test, utilizing a fold value of 3.
Keywords—Natural disasters, Twitter, Naïve Baiyes.
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