Explainable Artificial Intelligence (XAI) on Hoax Detection Using Decision Tree C4.5 Method for Indonesian News Platform

Hoax news can be defined as false information about events that tricks the readers into believing it as a genuine information. Explainable Artificial Intelligence or XAI is a simple algorithm that easy to understand. The example of XAI is Decision Tree. The methodology of this paper is Data Gathering to collect the data, preprocessing to give label to the parameters, Attribution Selection to determine which parameter to use by calculating the entropy and information gain result, then Decision Tree Construction using training datasets as a model for hoax detection, and calculating Evaluation Metric. This paper contains a total of 200 row of data (100 hoax and 100 fact). This research shows that the highest parameters for detecting hoax news are giving restless or panic emotion, containing hatred or angers, suggestion to share, and promising reward. By using 20% of the dataset for testing, the accuracy of this model is 82%.