Optimization of Random Forest Model via GridSearchCV for Hoax News Detection
Abstract
In this time of fast digital information growth, information sources can be helpful or harmful. The internet makes it easier for people to find information, but it also makes it easier for fake news and hoaxes to spread quickly and widely. This work seeks to combat the dissemination of false news in the digital age by employing text categorization through the Random Forest algorithm, coupled with hyperparameter optimization via Grid SearchCV.The dataset comprises both hoax and authentic news from Indonesia, subjected to various steps including text processing (case folding, tokenization, and stopword elimination) and feature weighting via TF-IDF.The study’s results reveal that the Random Forest model does an impressive job of telling the difference between fake and real news when tested using a confusion matrix. The confusion matrix shows that the model works better after hyperparameter tweaking with GridSearchCV. This is shown by the fact that the number of accurate predictions (TN and TP) goes up and the number of wrong predictions (FP and FN) goes down. The evaluation measures (accuracy, recall, precision, and F1-Score) also demonstrate significant improvements, increasing from 96% to 97%.
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