Analisi Komparasi Metode K-Nearest Neighbor dan Naïve Bayes Classifier Berbasis Optimasi Randomized Search Dalam Klasifikasi Berita Hoaks - Christopher, Bryan (Universitas Multi Data Palembang, 2025)
In the digital era, the spread of hoax news has become a serious challenge that can lead to misinformation and negative societal impacts. Therefore, effective methods are needed to accurately classify hoax news. This study compares the performance of the K-Nearest Neighbor (KNN) and Naïve Bayes Classifier (NBC) algorithms in hoax news classification, utilizing the Randomized Search optimization approach to improve model accuracy. The dataset used is the Indonesia False News (Hoax) Dataset from Kaggle, with input attributes consisting of news titles and narratives. The results show that Randomized Search optimization successfully enhanced the performance of both algorithms. Naïve Bayes demonstrated superior performance compared to KNN, achieving an accuracy of 84.77%, precision of 84.77%, recall of 84.77%, and a misclassification error of 15.23%. Meanwhile, KNN achieved an accuracy of 84.30%, precision of 82.88%, recall of 84.30%, and an error rate of 15.70%. Based on these findings, Naïve Bayes is more effective in detecting hoax news than KNN. This study contributes to the development of hoax news detection systems by optimizing models using Randomized Search, which can help reduce the spread of false information in society.