Optimasi Naive Bayes dan Cosine Similarity Menggunakan Particle Swarm Optimization Pada Klasifikasi Hoax Berbahasa Indonesia
Year: 2022
Type: article
Abstract: The widespread circulation of hoax news in the information technology era is increasingly troubling, therefore in this era an algorithm to classify hoax news is necessary, in this study researchers focused on optimizing the accuracy of hoax news classification in text documents. The algorithm that will be used is Naive Bayes and cosine Similarity which previously has been applied with particle swarm optimization algorithm. In this study, it was concluded that after feature selection using PSO in the Naive Bayes algorithm the accuracy obtained increased from 0.91 to 0.93 while in the cosine similarity algorithm the accuracy increased from 0.62 to 0.73 after feature selection using PSO
Institution: Universitas Amikom Yogyakarta
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4313020165