COMPARATIVE ANALYSIS OF SUPPORT VECTOR MACHINE AND RANDOM FOREST METHODS BASED ON RANDOMIZED SEARCH OPTIMIZATION IN HOAX NEWS CLASSIFICATION - Vincent Lawrence; Frans Mikael Sinaga (Program Studi Teknik Informatika Universitas Khairun, 2026)
In today’s digital era, the spread of hoax news has increasingly escalated alongside the ease of access to information through social media and online news portals. This phenomenon has caused negative impacts such as public confusion, social conflict, and a decline in public trust toward information accuracy. Therefore, an effective classification method is needed to accurately detect hoax news. This study aims to analyze and compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in classifying hoax news, by applying the Randomized Search optimization technique to enhance model performance. The dataset used in this research was obtained from Kaggle, titled Indonesia Fact and Hoax Political News, consisting of news titles and narratives as input attributes, and hoax or factual labels as outputs. The results show that the SVM algorithm without optimization achieved an accuracy of 83.92%, which increased to 84.28% after optimization using Randomized Search. Meanwhile, the Random Forest algorithm without optimization achieved an accuracy of 85.93%, which increased to 86.05% after optimization. Based on these findings, it can be concluded that the application of Randomized Search successfully improved the accuracy, sensitivity, and stability of the classification models, with the Random Forest algorithm providing the best performance in detecting hoax news in this study.