KLASTERISASI DATA TAKE-OFF PENERBANGAN DI BANDAR UDARA SULTAN HASANUDDIN MENGGUNAKAN ALGORITMA K-MEANS

AFRIANSYAH, INDRA GIFARI and Passarella, Rossi and Kurniati, Rizki (2023) KLASTERISASI DATA TAKE-OFF PENERBANGAN DI BANDAR UDARA SULTAN HASANUDDIN MENGGUNAKAN ALGORITMA K-MEANS. Undergraduate thesis, Sriwijaya University.

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Abstract

Sultan Hasanuddin Airport has an accident virginity rate with a total movement of 212,656 and a deviation value (the result of calculating the deviation between expected occurrence and recorded occurrence) of 3,540. This is due to many factors, such as natural factors, human errors, etc. Then it is necessary to classify flight data in the take-off phase in order to visualize the pattern of data spread based on the characteristics formed. The cluster method used in this research is K-Means which is an algorithm in the method of clusterization in data mining. K-Means group data into a cluster that has the same characteristics and the other characteristics are grouped into different clusters. Based on the results obtained from the research carried out using the K-Means cluster method, the results were given two classes of flight patterns, namely normal and abnormal. This is demonstrated by calculating the nearest distance by determining the centroid value randomly using the euclidean distance formula. The results of the analysis of the data provided a conclusive insight that C2 is a class with normal flight data, flights in the C2 class have ground speed sufficient to carry out flight and the distance of the aircraft from the end of the runway is still numerous and C1 is an abnormal class that flies at speeds faster than the minimum ground speed and flies in the half-to-end runway.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	ADS-B, Analisis, Data, Flight, K-Means
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. 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:	Mr Indra Gifari Afriansyah
Date Deposited:	26 Jul 2023 02:07
Last Modified:	26 Jul 2023 02:07
URI:	http://repository.unsri.ac.id/id/eprint/116934

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