PENGEMBANGAN MODEL KLASIFIKASI ABNORMALITAS PENERBANGAN MENGGUNAKAN METODE MACHINE LEARNING

PASSARELLA, ROSSI and Nurmaini, Siti and Firdaus, Firdaus (2023) PENGEMBANGAN MODEL KLASIFIKASI ABNORMALITAS PENERBANGAN MENGGUNAKAN METODE MACHINE LEARNING. Doctoral thesis, Sriwijaya University.

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Abstract

Aviation is one of the safest modes of transportation worldwide. However, there is still the possibility of flight abnormalities that can cause accidents. To prevent accidents, a system that can detect flight abnormalities early is needed. This dissertation discusses the development of a flight abnormality classification model using machine learning methods. The developed model uses flight data collected from the ADS-B data servers. The data were processed using 26 machine learning algorithmic methods to produce a classification model that can detect flight abnormalities with high accuracy. The results show that the selected model is the quadratic discriminant analysis (QDA) algorithm, which can detect flight abnormalities with an accuracy of 97%. This model can be used to improve flight safety by detecting abnormalities early. Keywords: Aviation Abnormalities, Classification, Machine Learning, Aviation Safety

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Aviation Abnormalities, Classification, Machine Learning, Aviation Safety
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) > T11 General works > T11.95 General works By region or country United States T Technology > T Technology (General) > T10.5-11.9 Communication of technical information > T11 General works > T11.95 General works By region or country United States T Technology > T Technology (General) > T11.95-12.5 Industrial directories > T11.95 General works By region or country United States
Divisions:	03-Faculty of Engineering > 21001-Engineering Science (S3)
Depositing User:	Rossi Passarella
Date Deposited:	26 Jan 2024 02:27
Last Modified:	26 Jan 2024 02:27
URI:	http://repository.unsri.ac.id/id/eprint/139834

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