What Can Be Done with Tweets of Twitter During A Disaster Situation? A Case of Tsunami Palu 2018

Pamuji, Rizky; Amrullah, Muhammad Erwin; Ramdani, Fatwa

doi:10.1109/siet48054.2019.8986116

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Natural disasters such as earthquakes [3], volcanic eruptions [4], and tsunamis can greatly impact and disrupt people's lives. In 2018, Palu was one area hit by a natural disaster [5], resulting in severe building damage.…”

Section: Introductionmentioning

confidence: 99%

Post natural disaster object recognition system using python-based scale invariant feature transform (SIFT) algorithm

Zakaria,

Fauzan,

Wahidin

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

The natural disaster in Palu became a significant challenge in identifying damaged building objects. The large number of damaged buildings made it difficult for the government to identify the level of damage to buildings and the feasibility of the construction process after natural disasters. This research is an object recognition system after natural disasters using the Scale Invariant Feature Transform (SIFT) algorithm implemented in Python programming language. This research aims to assist the government or National Search and Rescue Agency team in identifying the level of damage to buildings after natural disasters. The methods used in this research include literature study, image capture, processing, testing, and data analysis. The SIFT algorithm was selected to see the accuracy of comparisons of images of buildings before and after natural disasters. The test results show that the SIFT method can identify the average accuracy level of damage to buildings before and after natural disasters, reaching 98.24%.

show abstract

Section: Introductionmentioning

confidence: 99%