Skripsi

PENGEMBANGAN MODEL KLASIFIKASI ABNORMALITAS PENERBANGAN MENGGUNAKAN METODE MACHINE LEARNING

---

Penilaian

---

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

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2407001206	T139834	T1398342023	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1398342023

Publisher

Palembang : Prodi Doktor (S3) Ilmu Teknik, Teknik Sipil Universitas Sriwijaya., 2023

Collation

xx, 112 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

629.130 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Prodi Doktor (S3) Ilmu Teknik, Teknik Sipil
Aeronautikal

Specific Detail Info

-

Statement of Responsibility

MURZ

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment