Skripsi

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

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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.

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Detail Information

Series Title

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Call Number

T1597212024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

ix, 86 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

387.736 07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Teknik Informatika
Pendaratan Pesawat - Deteksi Anomali

Specific Detail Info

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Statement of Responsibility

TUTI

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