Klasifikasi Berita Online dengan menggunakan Pembobotan TF-IDF dan Cosine Similarity - Bening Herwijayanti; Dian Eka Ratnawati; Lailil Muflikhah (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
In discussing the online news by using the weighting of tf-idf and cosine of this similarity the previous research reference on online news information using single pass clustering algorithm, where the data to be used comes from the online news website that is kompas.com. Because of the many news that is on the website, so sometimes the news is posted not in accordance with the category. Human error will be the problem of wrong news posting. In addition to posting errors online news groupings are also important for the convenience of users to search for news according to their category. Implementing online news stories using tf-idf and cosine similarities, preprocessing processes ie tokenizing, stopword and stemming can reduce the term process of speeding the weighting of terms using tf-idf and accelerating the cosine process of similarity. The goal is to facilitate human error as well as reduce caution categorization. The value is able to classify news with accreditation rate of 91.25%.