PREDIKSI KETERLAMBATAN KEDATANGAN PENERBANGAN DENGAN METODE MACHINE LEARNING
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 compari...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/50685 |
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| Institution: | Institut Teknologi Bandung |
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id-itb.:506852020-09-24T23:15:40ZPREDIKSI KETERLAMBATAN KEDATANGAN PENERBANGAN DENGAN METODE MACHINE LEARNING Rizki Oktafianto, Muhammad Indonesia Final Project Flight delay, prediction model, machine learning, interpretable model, feature importance INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/50685 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. text |
| institution |
Institut Teknologi Bandung |
| building |
Institut Teknologi Bandung Library |
| continent |
Asia |
| country |
Indonesia Indonesia |
| content_provider |
Institut Teknologi Bandung |
| collection |
Digital ITB |
| language |
Indonesia |
| description |
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.
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| format |
Final Project |
| author |
Rizki Oktafianto, Muhammad |
| spellingShingle |
Rizki Oktafianto, Muhammad PREDIKSI KETERLAMBATAN KEDATANGAN PENERBANGAN DENGAN METODE MACHINE LEARNING |
| author_facet |
Rizki Oktafianto, Muhammad |
| author_sort |
Rizki Oktafianto, Muhammad |
| title |
PREDIKSI KETERLAMBATAN KEDATANGAN PENERBANGAN DENGAN METODE MACHINE LEARNING |
| title_short |
PREDIKSI KETERLAMBATAN KEDATANGAN PENERBANGAN DENGAN METODE MACHINE LEARNING |
| title_full |
PREDIKSI KETERLAMBATAN KEDATANGAN PENERBANGAN DENGAN METODE MACHINE LEARNING |
| title_fullStr |
PREDIKSI KETERLAMBATAN KEDATANGAN PENERBANGAN DENGAN METODE MACHINE LEARNING |
| title_full_unstemmed |
PREDIKSI KETERLAMBATAN KEDATANGAN PENERBANGAN DENGAN METODE MACHINE LEARNING |
| title_sort |
prediksi keterlambatan kedatangan penerbangan dengan metode machine learning |
| url |
https://digilib.itb.ac.id/gdl/view/50685 |
| _version_ |
1822000728989761536 |