Classification of Natural Disaster on Online News Data Using Machine Learning

We know neither what the future holds nor what will occur in the future. It is impossible to anticipate what the next instant may bring, and it could be awful. A natural disaster is an unforeseen event, which can have a tremendous impact on human life and the environment. The internet provides many sources that generate vast amounts of news articles daily. As the amount of news stories online increases, it's becoming more difficult for people to access disaster-relevant news, which makes it necessary to extract and classify news to be easily accessed. This paper presents an automated system that scraps news from various online sources and identifies disaster-relevant news. This paper also states the performance evaluation for classifying the natural disaster types based on machine learning in Indonesia's online news. Our results show that relevant Indonesian's online news about natural disasters can be the accuracy around 96% using the Support Vector Machine for three classes of natural disasters. In the Indonesian news data set, the machine learning algorithm that gets the highest value out of all the parameters is the best.