Maharani, Indira (2025) Optimasi Rute Unmanned Aerial Vehicle Dalam Mencegah Penyebaran Kebakaran Semak Di Wilayah Metropolitan Perth Menggunakan Algoritma Ant Colony. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kebakaran semak yang sering terjadi di Wilayah Metropolitan Perth menimbulkan risiko tinggi terhadap keselamatan dan lingkungan sekitarnya. Untuk mendukung upaya deteksi dini serta pencegahan penyebaran api, tugas akhir ini mengembangkan model optimasi rute inspeksi menggunakan UAV DJI Matrice 300 RTK dengan pendekatan Multi-Depot Vehicle Routing Problem (MDVRP). Empat pendekatan algoritma diterapkan, yaitu Ant Colony Optimization (ACO), Ant Colony System (ACS), serta variasinya yang dikombinasikan dengan metode Local Search (ACO-LS dan ACS-LS). Hasil pengujian menunjukkan bahwa sebelum dilakukan modifikasi, algoritma ACS mampu menghasilkan total jarak tempuh yang lebih pendek dibandingkan ACO. Setelah penambahan teknik Local Search, ACS-LS menunjukkan performa terbaik dalam meminimalkan total jarak dan durasi rute dibandingkan dengan algoritma lainnya pada empat skenario yang diuji. Dengan demikian, kombinasi ACS dan Local Search terbukti menjadi pendekatan paling efektif untuk optimasi rute UAV dalam upaya pencegahan penyebaran kebakaran semak.
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Bushfires that frequently occur in the Perth Metropolitan Area pose a significant risk to public safety and the surrounding environment. To support early detection and the prevention of fire spread, this final project develops an inspection route optimization model using the UAV DJI Matrice 300 RTK with the Multi-Depot Vehicle Routing Problem (MDVRP) approach. Four algorithmic approaches are applied: Ant Colony Optimization (ACO), Ant Colony System (ACS), and their respective variants combined with the Local Search method (ACO-LS and ACS-LS). Experimental results show that prior to modification, the ACS algorithm produces shorter total travel distances than ACO. After the integration of Local Search techniques, ACS-LS demonstrates the best performance in minimizing total distance and travel duration across the four tested scenarios. Therefore, the combination of ACS and Local Search is proven to be the most effective approach for UAV route optimization in efforts to prevent the spread of bushfires.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Algoritma Ant Colony, Local Search, MDVRP, Optimasi Rute, UAV, Ant Colony Algorithm, Local Search, MDVRP, Optimization, UAV |
| Subjects: | T Technology > T Technology (General) > T57.84 Heuristic algorithms. |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis |
| Depositing User: | Indira Maharani |
| Date Deposited: | 25 Jul 2025 14:02 |
| Last Modified: | 25 Jul 2025 14:02 |
| URI: | http://repository.its.ac.id/id/eprint/121730 |
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