JARO WINKLER ALGORITHM FOR MEASURING SIMILARITY ONLINE NEWS - Teguh Efriyanto; Mardhiya Hayaty (Jurnal Teknik Informatika (Jutif), 2022)
Online news is a source of information for people; this impacts journalists as news writers who can find news information quickly and accurately every day. Journalists can plagiarise other journalists or take news material from other news media sites and use it to publish in the media without including the source. An algorithm is needed to measure the similarity of online news. This work proposed the Jaro Winkler algorithm, with the value obtained from the calculation normalised so that the value 0 means there is no resemblance, and one means it has the exact resemblance. The data used is 20 online news media sites in the Central Kalimantan area. The Scraping process utilised the Custome Search JSON API and used keywords to get the news on the same topic. The results of the calculation of news similarity with the Jaro Winkler algorithm obtained an average value of online news similarity of 74.49%, with 43 news data with severe plagiarism levels and 12 news data with moderate plagiarism levels. There are weaknesses in the Jaro Winkler algorithm in calculating the similarity value in the data obtained. Some undetected data should have a heavy plagiarism level but not severe and vice versa.