Query Expansion Pada LINE TODAY Dengan Algoritme Extended Rocchio Relevance Feedback - Chandra Ayu Anindya Putri; Indriati Indriati; Ahmad Afif Supianto (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
LINE TODAY provides access to up-to-date news contents. Data on LINE TODAY are used to be able to do search engine feature. Query Expansion technique will be very useful if it is to be combined with search engine system where the queries inputted by users are combined with additional queries from the system. These additional queries will make queries generated by users more specific. In addition, users feedback (user judgement/explicit relevance feedback) assessing on each news can minimize ambiguous queries. The process begins with preprocessing technique consisting of several stages which are cleansing, case folding, tokenization, filtering, and stemming. And then, term weighting and cosine similarity. The next process is calculated using the Extended Rocchio Relevance Feedback method which is a traditional method from Rocchio Relevance Feedback to generate an additional queries. The results are obtained from implementation and testing process of Query Expansion on LINE TODAY with Extended Rocchio Relevance Feedback Algorithm resulted an average Precision value of 0.53308, Recall value of 0.81708, F-Measure value of 0.59553, and Accuracy value of 0.9574. The accuracy value obtained with Extended Rocchio Relevance Feedback method based on user judgement increase by 2% compared to automated search by the method of Rocchio Relevance Feedback.