DETEKSI EVENT PADA HASHTAG TWITTER MENGGUNAKAN DF-IDF DAN ENTROPI WAVELET - Amin Ajaib Maggang (Jurnal Media Elektro, 2021)
This study aims to detect event from a collection of tweets having same hashtag. Discrete Wavelet Transform (DWT) and Document Frequency-Inverse Document Frequency (DF-IDF) techniques were utilised in this research to develop and analyse the signal. Signals are developed using DF-IDF during a certain period while DWT is applied to capture the sudden change in the DF-IDF signal and display it in the form of entropy. Words that have a sudden change in signal value at the same period of time might represent an event related to the topic of the hashtag. Knowing the words will assist users to discover the event associated to the hashtag. Using 3473 tweets collected on the RIPLKY hashtag with the 20 most frequently occurring words, the results showed that DWT managed to capture the sudden change in df-idf signal related to a spike occured in the wavelet entropi. The spike was shown by three words, namely proud, sophialimx, and goals. We also found that the three words were under the same tweet, and it describes an event about the funeral of Singaporean prime minister.