Prayoga, Daffa Rheza (2026) Optimasi Rute Unmanned Aerial Vehicle Dalam Penanganan Stressed Region Pada Precision Agriculture Dengan Menggunakan Algoritma Ant Colony Optimization. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Pertumbuhan populasi yang pesat serta meningkatnya kebutuhan pangan nasional memerlukan inovasi di bidang pertanian untuk meningkatkan produktivitas secara efisien dan berkelanjutan. Penelitian ini bertujuan untuk mengoptimalkan rute Unmanned Aerial Vehicle (UAV) dalam penanganan stressed region pada lahan pertanian dengan memodelkan permasalahan sebagai Vehicle Routing Problem (VRP). Proses penelitian melibatkan dua tahap utama: pertama, penentuan titik target menggunakan metode klasterisasi Lloyd's Algorithm (K Means) yang dioptimalkan dengan grid search untuk memaksimalkan cakupan area. Kedua, optimasi rute untuk mengunjungi titik-titik tersebut menggunakan algoritma hibrida yang menggabungkan Max-Min Ant System (MMAS) dengan heuristik local search 2-Opt. Kinerja algoritma yang diusulkan kemudian divalidasi dengan membandingkannya terhadap solver standar industri, Google OR-Tools. Hasil penelitian menunjukkan bahwa tahap klasterisasi berhasil menentukan 76 titik target dengan cakupan area mencapai 97.60%. Algoritma hibrida MMAS + 2-Opt mampu menghasilkan rute terpendek sepanjang 1345.07 meter yang sangat layak secara operasional, dan terbukti sangat kompetitif dengan selisih hanya 0.08% dari solusi yang dihasilkan oleh OR-Tools. Penelitian ini menunjukkan bahwa pendekatan yang diusulkan efektif dan andal untuk perencanaan rute UAV dalam mendukung implementasi precision
agriculture.
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Rapid population growth and increasing national food demands necessitate innovation in the agricultural sector to enhance productivity efficiently and sustainably. This study aims to optimize the route of an Unmanned Aerial Vehicle (UAV) for handling stressed regions in agricultural fields by modeling the problem as a Vehicle Routing Problem (VRP). The research process involves two main stages: first, determining target points using the Lloyd's Algorithm (K-Means) clustering method, optimized with a grid search to maximize area coverage. Second, route optimization to visit these points is performed using a hybrid algorithm that combines the Max-Min Ant System (MMAS) with a 2-Opt local search heuristic. The performance of the proposed algorithm was then validated by comparing it against an industry-standard solver, Google OR-Tools. The results show that the clustering stage successfully identified 76 target points, achieving an area coverage of 97.60%. The hybrid MMAS + 2-Opt algorithm generated a shortest route of 1345.07 meters, which is highly feasible operationally, and proved to be extremely competitive with a difference of only 0.08% from the solution produced by OR-Tools. This study demonstrates that the proposed approach is effective and reliable for UAV route planning in support of precision agriculture implementation.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Precision Agriculture, Stressed Region, Unmanned Aerial Vehicle, Ant Colony Optimization, Max-Min Ant System, Optimasi Rute, Vehicle Routing Problem |
| Subjects: | H Social Sciences > HE Transportation and Communications > HE336.R68 Route choice 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: | Daffa Rheza Prayoga |
| Date Deposited: | 30 Jan 2026 02:07 |
| Last Modified: | 30 Jan 2026 02:07 |
| URI: | http://repository.its.ac.id/id/eprint/131319 |
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