Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal FRAMEWORK Jurnal Ilmu Komputer dan Informatika
NABILLA RIZQI AMALIA NUR ASRI
UNIVERSITAS MUHAMADIYAH SORONG
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Deteksi Teks Berita Hoax Bahan Bakar Minyak (BBM) Menggunakan Metode K-Nearest Neighbor NABILLA RIZQI AMALIA NUR ASRI; Rendra Soekarta; Muhammad Yusuf
Framework : Jurnal Ilmu Komputer dan Informatika Vol. 1 No. 02 (2023): Framework : Jurnal ilmu komputer dan Informatika
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/jiki.v1i02.2746

Abstract

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%.
Co-Authors Muhammad Yusuf Soekarta, Rendra