Mengiplementasikan Vector Space Model Similarity Euclidean Distance Menggunakan TFIDF Pada Klasifikasi Teks Bahasa Indonesia

Abstract

Weighting based on the term with stemming techniques to get the basic word form term in question. This will the application of the Indonesian language text classification machine using the K-Nearest Neighbor algorithm and the Vector Space Model method on the TFIDF frequency weighting of the number of words and the Euclidean Distance function. comparison between the test documents and the test sample collection Using news documents as learning documents, a total of 10 (10) documents with 3 (three) categories, produces an Precision and Recall 90.00% for k = 5 using frequency weighting in words with the Euclidean Distance function.