Skripsi

ANALISIS PERBANDINGAN ALGORITMA ANN, ANN-PSO, DAN ANN-GA UNTUK KLASIFIKASI KETERLAMBATAN KEBERANGKATAN PESAWAT

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Flight departure delays affect operational efficiency and the quality of air transportation services. This study compares the performance of the Artificial Neural Network (ANN), ANN optimized using Particle Swarm Optimization (ANN-PSO), and ANN optimized using Genetic Algorithm (ANN-GA) for flight delay classification using a two-class dataset, namely on-time and delayed flights, based on operational features. The optimization of ANN weights is performed using Particle Swarm Optimization and Genetic Algorithm, with model performance evaluated using accuracy, precision, recall, and F1-score metrics. The results show that ANN-PSO achieves the best performance, with an accuracy of 88.66%, precision of 92.42%, recall of 84.24%, and an F1-score of 88.14%, indicating a good balance between precision and recall. Future research is recommended to explore other optimization algorithms and to utilize larger datasets to further improve model performance and generalizability. Keywords: Artificial Neural Network, classification, flight delay, Genetic Algorithm, Particle Swarm Optimization.

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Detail Information

Series Title

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Call Number

T1931722026

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xiv, 79 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.320 7

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Perbandingan Algoritma

Specific Detail Info

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Statement of Responsibility

MI

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