KLASIFIKASI ANOMALI PROSES LANDING PESAWAT TERBANG PADA BANDARA SULTAN SYARIF KASIM II MENGGUNAKAN METODE KNN

ADITYA, ADITYA and Passarella, Rossi and Arsalan, Osvari (2023) KLASIFIKASI ANOMALI PROSES LANDING PESAWAT TERBANG PADA BANDARA SULTAN SYARIF KASIM II MENGGUNAKAN METODE KNN. Undergraduate thesis, Sriwijaya University.

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Abstract

Air transportation still has the possibility of having an accident. According to Boeing, fatal accidents on flights occur mostly in the final approach phase, which is 28%, and the landing phase, which is 26%. In Indonesia, Sultan Syarif Kasim II Airport is in 6th place out of the 10 most dangerous airports, according to research written by Sandhyvavitri in 2014. According to the NTSC, from 2010 to 2016, 67% of aircraft accidents were caused by humans. Because of this, an analysis and modeling of ADS-B data classification were carried out to determine which aircraft violated the touchdown rules at Sultan Syarif Kasim II airport using the K-Nearest Neighbor (KNN) method. KNN is a machine learning method that classifies data based on the dominant class of the nearest neighbor. In this research, KNN is tested by utilizing a confusion matrix that produces accuracy, precision, recall, and f1-score values. The results of KNN testing using a ratio of 70% training data and 30% testing data from k = 2 to 11 values show that the k = 5 configuration has the best performance with an average evaluation metric value for each test of 99%. Keywords: K-nearest neighbor, machine learning, ADS-B, flight, landing, Sultan Syarif Kasim II.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	K-Nearest Neighbour, Machine Learning, ADS-B, Penerbangan, Landing, Sultan Syarif Kasim II.
Subjects:	T Technology > T Technology (General) > T55-55.3 Industrial safety. Industrial accident prevention > T55.25 Research
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Aditya Aditya
Date Deposited:	26 Jul 2023 08:46
Last Modified:	26 Jul 2023 08:46
URI:	http://repository.unsri.ac.id/id/eprint/122319

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