DEVELOPMENT OF FLIGHT DELAY PREDICTION SYSTEM USING SUPERVISED MACHINE LEARNING
Flight delays are a significant and escalating issue as the aviation sector records continuous growth. These delays cause losses for passengers, airlines, and airports. Although several flight delay prediction models have been developed, their performances remain rather low. This research aims to...
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| Format: | Final Project |
| Language: | Indonesia |
| Subjects: | |
| Online Access: | https://digilib.itb.ac.id/gdl/view/82297 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Flight delays are a significant and escalating issue as the aviation sector records continuous
growth. These delays cause losses for passengers, airlines, and airports. Although several flight
delay prediction models have been developed, their performances remain rather low. This
research aims to create an explainable artificial intelligence (XAI) flight delay prediction model
using machine learning. The methods employed in the research include formulating delay
categories, comparing several machine learning methods, selecting the best model, and
conducting feature influence analysis using the SHAP (Shapley Additive Explanations) method.
The data employed is domestic flight data in the United States from 2021 to 2022. The research
results in a prediction model using the random forest algorithm, achieving an accuracy of 83%
and an F1 score of 0.83. SHAP analysis identifies key factors influencing delays, including
turnaround time, previous delay duration, airline, airport delay, and flight distance. These
findings are beneficial for airlines to improve on-time performance (OTP) and for airports to
enhance slot management. |
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