Pemanfaatan Vector Space Model Algoritma Nazief Andriani Pembobotan Tfidf Pada Prototipe Klasifikasi Teks Bahasa Indonesia

Abstract

Information Retrieval is one of the IR techniques that will be used to present the dummy profile is the vector space model where n is the sum of all the terms in the list. To overcome this problem, one technique that can be used is to classify the text of the document in accordance with characteristics, features, and classes based on the standard rules of the language to be processed. In this study, Indonesian is the language used as a reference source. The object of this research is the Indonesian Language Text document. This study will test the application of text classification engine of Indonesian language using Stemming Nazief Andriani algorithm, K-Nearest Neighbor algorithm and Vector Space Model Model based on frequency weighting of TFIDF number of words and Simpson functions. By using document news as document learning, as many as 15 (fifteen) documents with 3 (three) categories, yielding the average value of Precision and Recall of 81.33%.