Deteksi Teks Berita Hoax Bahan Bakar Minyak (BBM) Menggunakan Metode K-Nearest Neighbor

NABILLA RIZQI AMALIA NUR ASRI (UNIVERSITAS MUHAMADIYAH SORONG)
Rendra Soekarta (Universitas Muhammadiyah Sorong)
Muhammad Yusuf (Universitas Muhammadiyah Sorong)

FRAMEWORK Jurnal Ilmu Komputer dan Informatika
Vol. 1 No. 02 (2023): Framework : Jurnal ilmu komputer dan Informatika

Hoax is information that is untrue and dangerous because it deceives people into believing something that is not true. Hoaxes can make people uneasy because of information that is not known to be true. because the development of information and communication technology also allows hoaxes to circulate quickly. In this research a system is needed that aims to minimize anxiety that will occur by distinguishing hoax news from non-hoax news. Text mining is a type of data mining that looks for interesting patterns in collections of text data. The method used in text mining is k-nearest neighbor (KNN). The process of detecting hoax news can be done with the data collection stage, data preprocessing dividing the data by 80% data train and 20% test data then classifying with k-nearest neighbors with a value of k = 1. The accuracy results obtained are 70.83%.