Detecting Hoax News with Web-Based SVM Classifier: A Machine Learning Approach to Combat Misinformation Using Support Vector Machine (SVM) Algorithm - Auliana, Sigit; Kalsum, Ummy; Mudofar, Mudofar; Permana, Basuki Rahim Setya (JINAV: Journal of Information and Visualization, 2025)
The massive spread of hoax news in the digital era has caused various negative impacts, ranging from public misinformation to social unrest. This condition highlights the need for a technological solution capable of automatically and accurately detecting the truthfulness of information. This study aims to develop a web-based application for hoax news detection using the Support Vector Machine (SVM) algorithm. The method involves text preprocessing, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), and classification with SVM. The system is designed with a simple and responsive web interface, allowing users to input news content and receive prediction results easily. Testing was conducted using both white-box and black-box methods to ensure that the system's logic functioned as expected. The results show that the system successfully classifies news into two categories—valid and hoax—with good accuracy. This application is expected to serve as a useful tool for the public in identifying the authenticity of news and preventing the spread of false information across digital platforms.