Perbandingan Algoritma ANN, KNN dan Decision Tree untuk Klasifikasi Delay Airlines - Jason Sunaryo; Teny Handhayani (Faculty of Information Technology, Universitas Tarumanagara, 2026)
As time progresses, transportation methods become more sophisticated. One of the commonly used modes of long-distance travel is by air travel. However, there are occasional challenges encountered, one of which is delays. Airlines delays not only inconvenience passengers but also incur losses for airlines due to increased operational costs. Therefore, there is a need to predict whether a flight will be delayed or not. This research aims to predict delays using three algorithms: ANN, Decision Tree, and KNN. The dataset utilized in this study consists of 539,383 records. The findings of this study indicate that the most suitable algorithm for this dataset is the Decision Tree algorithm, with an average accuracy of 62.5%.