<![CDATA[The Random Forest algorithm is an ensemble-based machine learning method widely used to improve the accuracy of predictive models. This algorithm works by randomly constructing many decision trees and combining the results to produce more accurate predictions and reduce the risk of overfitting. The advantages of Random Forest lie in its ability to handle complex datasets, manage variables with high correlation, and provide stable results in various scenarios. This study aims to analyze the performance of the Random Forest algorithm in overcoming overfitting and improving the accuracy of predictive models in various fields. The method used in this study is a literature study (library research), by collecting and analyzing 40 scientific literature from various sources such as international journals, proceedings, and relevant academic articles. Data were analyzed qualitatively with a comparative-descriptive approach to the advantages and disadvantages of Random Forest compared to other algorithms such as Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, and Neural Networks. The results show that Random Forest excels in handling high-dimensional data, reduces the risk of overfitting, and provides stable prediction results in various applications such as healthcare, finance, manufacturing, and environmental analysis. This research is limited to literature-based analysis without empirical testing using actual datasets. For future research, it is recommended to conduct direct experiments implementing the Random Forest algorithm on real-world datasets, as well as explore combinations of other ensemble algorithms, such as XGBoost or LightGBM, to improve the accuracy and efficiency of predictive models.]]>
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