This research aims to compare the effectiveness of two stock price prediction algorithms, namely FB Prophet and Random Forest, using the CRISP-DM method. The main focus of the study is on the stocks of PT XYZ, with data taken from the period of March 1, 2019, to March 1, 2024. Stock price prediction is a significant topic in the field of finance as it can help investors make better decisions. Both algorithms are evaluated based on error rates measured using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). This study demonstrates that both FB Prophet and Random Forest algorithms have their respective advantages in predicting stock prices. This research is also expected to contribute to the scientific literature in the fields of data mining and stock price prediction analysis.