Analysis of Hoax News in News Media Using the K-Nearest Neighbor (K -NN) Method

Abstract

The rapid development of internet technology has facilitated the dissemination of information within the community through various news media. However, this convenience has also fueled the widespread dissemination of unverified fake news (hoaxes), potentially causing social unrest, undermining the credibility of information, and manipulating public opinion. Therefore, a computing system capable of automatically filtering and categorizing news is needed to help readers distinguish between facts and hoaxes. This study aims to develop an automatic classification model to identify hoax news using the K-Nearest Neighbor (KNN) method and measure its accuracy.

This study uses the Euclidean Distance approach with a value of k = 3. This study uses 2,000 news data that have been collected and verified through official fact-checking sites. The data was divided using the train-test split method with a ratio of 80:20 to obtain 1,600 training data and 400 testing data . Based on the results of the confusion matrix test at a value of K = 3, the True Positive (TP) value was 104, False Positive (FP) was 0, False Negative (FN) was 95, and True Negative (TN) was 201. The model performance evaluation showed a precision value of 100%, recall of 52.26%, and accuracy of 76.25%. These results indicate that the K-NN method is able to classify news well and has a high level of precision, although the model's ability to detect all hoax news ( recall ) still needs to be improved in further research. This research is expected to contribute as a technology-based tool ( decision support system ) for the public and academics in monitoring, filtering, and tackling the spread of fake news in digital media.