DETEKSI ANOMALI PENDARATAN PESAWAT DI BANDARA SULTAN SYARIF KASIM II MENGGUNAKAN ALGORITMA KMEANS, GAUSSIAN MATRIX MIXTURE (GMM), DAN BALANCED ITERATIVE REDUCING AND CLUSTERING USING HIERARCHIES (BIRCH) BERDASARKAN DATA ADS-B

NOOR, TASWIYAH MARSYAH and Passarella, Rossi and Arsalan, Osvari (2024) DETEKSI ANOMALI PENDARATAN PESAWAT DI BANDARA SULTAN SYARIF KASIM II MENGGUNAKAN ALGORITMA KMEANS, GAUSSIAN MATRIX MIXTURE (GMM), DAN BALANCED ITERATIVE REDUCING AND CLUSTERING USING HIERARCHIES (BIRCH) BERDASARKAN DATA ADS-B. Undergraduate thesis, Sriwijaya University.

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Abstract

Accidents in commercial aviation in Indonesia have increased to 30% of total transport users by 2023. One important factor to reduce the risk of accidents and financial losses is compliance with landing procedures. The main objective of this analysis is to raise awareness of the importance of complying with these rules to minimise the risk of accidents and losses. Clustering techniques were applied for data analysis, using kmeans, GMM, and BIRCH methods. Based on the evaluation conducted using the silhouette score, Davies-Bouldin index (DBI), and Calinski�H arabasz index (CHI), the GMM method proved to be the most stable in forming clusters. Anomaly analysis showed that 17.6% of the data were identified as anomalies based on the vertical velocity rule, while anomalies based on elevation accounted for only 0.8% of the total data. The limitations of this analysis lie in the small number of features analysed, namely vertical velocity and elevation, and the three-month data collection period. For future research, it is recommended to add more features, extend the data collection period, and use more algorithms to improve the accuracy and validity of the analysis.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	KMeans, GMM, Birch, Silhouette Score, CHI, DBI, Anomali, clustering, ADS-B, Landing, Sultan Syarif Kasim II
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Taswiyah Marsyah Noor
Date Deposited:	22 Nov 2024 01:59
Last Modified:	22 Nov 2024 01:59
URI:	http://repository.unsri.ac.id/id/eprint/159721

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