PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM KLASIFIKASI JUDUL BERITA HOAX - Muhammad Diki Hendriyanto; Betha Nurina Sari (LPPM Universitas Putera Batam, 2022)
With the rapid development of information technology, especially in Indonesia, information is more easily obtained through online media. Therefore, the dissemination of information in online media becomes uncontrollable and a lot of information is not in accordance with the facts or can be said to be a hoax. Readers should be more careful when reading news headlines to avoid hoaxes. The purpose of this research is to find out how to apply the K-Nearest Neighbor (KNN) algorithm in classifying news including hoaxes or not hoaxes. In the process, the classification of hoaxes or non-hoaxes uses the KDD method in text mining and goes through several stages, namely preprocessing, word weighting with TF-IDF and classification using the KNN algorithm. There are 3 scenarios in the data split process, namely 90:10, 80:20, and 70:30. Evaluation is done by using a confusion matrix. The results of this study obtained the highest accuracy of 93.33% with a k value of 3 in the 90:10 scenario. So, the K-Nearest Neighbor algorithm is suitable for classifying hoax news titles.