TF-IDF Method and Vector Space Model Regarding the Covid-19 Vaccine on Online News

Abstract

Advances in information and technology have caused the use of the internet to be a concern of the general public. Online news sites are one of the technologies that have developed as a means of disseminating the latest information in the world. When viewed in terms of numbers, newsreaders are very sufficient to get the desired information. However, with this, the amount of information collected will result in an explosion of information and the possibility of information redundancy. The search system is one of the solutions which expected to help in finding the desired or relevant information by the input query. The methods commonly used in this case are TF-IDF and VSM (Vector Space Model) which are used in weighting to measure statistics from a collection of documents on the search for some information about the Covid 19 vaccine on kompas.com news then tokenizing it to separate the text, stopword removal or filtering to remove unnecessary words which usually consist of conjunctions and others. The next step is sentence stemming which aims to eliminate word inflection to its basic form. Then the TF-IDF and VSM calculations were carried out and the final result are news documents 3 (DOC 3) with a weight of 5.914226424; news documents 2 (DOC 2) with a weight of 1.767692186; news documents 5 (DOC 5) with weights 1.550165096; news document 4 (DOC 4) with a weight of 1.17141223;, and the last is news document 1 (DOC 1) with a weight of 0.5244103739.