PREDIKSI PENETAPAN TARIF PENERBANGAN MENGGUNAKAN AUTO-ML DENGAN ALGORITMA RANDOM FOREST - Yakub Anuyuta Zebua; Daniel Ryan Hamonangan Sitompul; Stiven Hamonangan Sinurat; Andreas Situmorang; Ruben Ruben; Dennis Jusuf Ziegel; Evta Indra (Politeknik Bisnis Indonesia, 2022)
With so many airlines competing with each other, airlines are competing to become the consumer/market's main choice, but to achieve this, there is no airline strategy that can predict the price of airline tickets according to market needs. To meet the needs of airlines, we need a way to determine the price of airline tickets according to market needs with the help of the influence of technology and information. This research method was carried out using Google Collaboratory as a media to create a data model automated machine learning (AutoML) with the Random Forest, Logistic Regression and Gradient Boosting Regressor algorithms. In this study, the model that produced the highest R2 value and the lowest RMSE was a random forest with an R2 value of 83.91% and an RMSE of 175.9. However, from the three models, Random Forest got a change in accuracy of 1.96% to 85.87. To assist in predicting the determination of flight fares, airline companies can more easily and be alert to determine flight fares that are in accordance with the market. Therefore, Random Forest can be declared better than Logistic Regression and Gradient Boosting models. The Random Forest model that has been created can be used to predict in real-time using Machine Learning.