PREDIKSI KETERLAMBATAN KEDATANGAN PENERBANGAN DENGAN METODE MACHINE LEARNING - Rizki Oktafianto, Muhammad
Flight Delay is a problem in Indonesia's growing aviation industry. The delay causes unscheduled traffic congestion and lowers the level of service in the air traffic controller. This study aims to create a flight prediction model using machine learning. The method in this study is a comparison of the implementation of 11 machine learning algorithms on flight data to Bandung to obtain prediction results for delays and model analysis using Local Interpretable Model-Agnostic Explanations (LIME). The best results from modeling with machine learning show the achievement level of the accuracy value of 91,5% and the AUC value on the ROC graph of 0,966 on the bagging classifier model with decision trees basis. Analysis of feature importance shows that the most significant factor is traffic at origin airport. The results of this study can be used to create a flight delay prediction system.