<![CDATA[This study aims to conduct a comparative analysis of the performance of two classification algorithms, namely Decision Tree and Random Forest, in predicting the level of airline passenger satisfaction. The data used in this research were obtained from the Airline Passenger Satisfaction dataset available on Kaggle, which contains various variables related to passengers’ flight experiences. The research employed a quantitative experimental method using the CRISP-DM (Cross Industry Standard Process for Data Mining) approach, consisting of several stages including data understanding, data preparation, modeling, evaluation, and deployment. The modeling process was carried out using RapidMiner Studio, with the dataset divided into 70% for training and 30% for testing. The experimental results indicate that the Decision Tree algorithm achieved an accuracy rate of 91.77%, while the Random Forest algorithm achieved a higher accuracy of 93.37%. This difference demonstrates that Random Forest possesses better generalization capabilities and more stable performance in handling complex and varied data. Therefore, it can be concluded that the Random Forest algorithm performs more effectively in predicting airline passenger satisfaction levels. Moreover, this study highlights the importance of selecting an appropriate algorithm in data analysis processes to support data-driven decision-making within the aviation industry.]]>
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