Optimasi Rute Unmanned Aerial Vehicle Dalam Penanganan Stressed Region Pada Precision Agriculture Dengan Menggunakan Algoritma Simulated Annealing

Aschafitz, Naufal Makarim (2025) Optimasi Rute Unmanned Aerial Vehicle Dalam Penanganan Stressed Region Pada Precision Agriculture Dengan Menggunakan Algoritma Simulated Annealing. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Ketahanan pangan menjadi tantangan besar di tengah pertumbuhan penduduk Indonesia yang mencapai 281,6 juta jiwa pada 2024 dengan laju 1,25% per tahun. Sementara itu, luas lahan pertanian terus menurun akibat alih fungsi dan urbanisasi, sehingga memengaruhi kapasitas produksi pangan nasional. Precision agriculture hadir sebagai solusi teknologi yang meningkatkan produktivitas dan efisiensi penggunaan sumber daya, salah satunya dengan pemanfaatan Unmanned Aerial Vehicle (UAV) untuk pemetaan stressed region serta distribusi agrokimia yang lebih presisi. Namun, penelitian terkait optimasi rute UAV pada sektor ini masih terbatas, khususnya dalam penerapan klasterisasi dan algoritma metaheuristik secara terpadu. Tugas akhir ini mengembangkan metode optimasi rute UAV menggunakan hybrid Simulated Annealing (SA) yang menggabungkan inisialisasi greedy dan local search two-opt untuk meminimalkan jarak tempuh distribusi agrokimia. Tahap klasterisasi dilakukan menggunakan Lloyd algorithm yang divalidasi melalui grid search sehingga diperoleh coverage ratio sebesar 97.28% dan overlap ratio 19.96%, memastikan seluruh area terdampak terjangkau secara efisien. Pengujian pada data lahan pertanian menunjukkan hybrid SA mampu menghasilkan rute terpendek sejauh 1425.22 meter dengan estimasi waktu penerbangan 203.6 detik, hanya berselisih 0.02% dibanding benchmark Google OR-Tools. Hasil ini membuktikan bahwa hybrid SA merupakan pendekatan kompetitif sekaligus adaptif untuk optimasi rute UAV pada precision agriculture, dengan potensi besar diterapkan dalam praktik ditribusi agrokimia di lapangan.
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Food security poses a significant challenge amid Indonesia’s growing population, which reached 281.6 million in 2024 with an annual growth rate of 1.25%. Meanwhile, the area of agricultural land continues to decline due to land conversion and urbanization, undermining the nation’s food production capacity. Precision agriculture emerges as a technology-driven approach to enhance productivity and resource efficiency, including the use of Unmanned Aerial Vehicles (UAVs) for mapping stressed regions and precisely distributing agrochemicals. However, studies focusing on UAV route optimization in this sector remain scarce, especially those that integrate clustering methods with metaheuristic algorithms. This research develops a UAV route optimization approach by modeling agrochemical distribution as a Vehicle Routing Problem (VRP), solved using a hybrid Simulated Annealing (SA) algorithm that combines greedy initialization with two-opt local search to minimize total travel distance. The clustering stage employs Lloyd’s algorithm validated by grid search, achieving a coverage ratio of 97.28% with an overlap of 19.96%, ensuring efficient spraying across affected areas. Tests on agricultural land data reveal that the hybrid SA produces a shortest route of 1,425.22 meters with an estimated flight time of 203.6 seconds, differing by only about 0.02% from the Google OR-Tools benchmark. These findings highlight the competitiveness and adaptability of the hybrid SA approach for UAV route optimization in precision agriculture applications.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Precision Agriculture, Stressed Region, Unmanned Aerial Vehicle, Simulated Annealing, Optimasi Rute, Precision Agriculture, Stressed Region, UAV, Simulated Annealing, Route Optimization
Subjects:	H Social Sciences > HE Transportation and Communications > HE336.R68 Route choice T Technology > T Technology (General) > T57.84 Heuristic algorithms.
Divisions:	Faculty of Information Technology > Information System > 57201-(S1) Undergraduate Thesis
Depositing User:	Naufal Makarim Aschafitz
Date Deposited:	25 Jul 2025 04:30
Last Modified:	25 Jul 2025 04:30
URI:	http://repository.its.ac.id/id/eprint/121397

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