Cristianto, Nikolaus Vico (2025) Optimasi Rute Unmaned Aerial Vehicle Dalam Penanganan Stressed Region Pada Precision Agriculture Menggunakan Algoritma Hybrid Genetic Algoritm-Variable Neighborhood Search. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sektor pertanian memiliki peran strategis dalam menjamin ketahanan pangan nasional, terutama dengan meningkatnya populasi Indonesia yang diproyeksikan mencapai 381 juta jiwa pada tahun 2045. Penerapan precision agriculture dengan dukungan teknologi Unmanned Aerial Vehicle (UAV) menjadi solusi efektif dalam meningkatkan efisiensi produksi pertanian melalui distribusi pestisida yang tepat sasaran pada wilayah tanaman yang mengalami stres (stressed region). Namun, perencanaan rute UAV yang efisien masih menjadi tantangan utama dalam implementasinya. Tugas Akhir ini bertujuan mengembangkan algoritma Hybrid Genetic Algorithm - Variable Neighborhood Search (HGA-VNS) untuk mengoptimasi rute UAV dalam konteks precision agriculture. Metodologi meliputi pengumpulan data sekunder, klasterisasi wilayah stressed region menggunakan algoritma Lloyd, serta proses optimasi rute dengan HGA-VNS. Hasil pengujian menunjukkan bahwa algoritma HGA-VNS mampu meningkatkan efisiensi rute dengan jarak rata-rata sebesar 1961,29 meter, lebih baik dibandingkan algoritma benchmark Google OR-Tools yang menghasilkan rata-rata jarak 1970,48 meter. Dengan demikian, algoritma HGA-VNS yang dikembangkan dalam tugas akhir ini memiliki potensi untuk diterapkan secara praktis dalam mendukung penerapan precision agriculture yang lebih efisien.
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The agricultural sector plays a strategic role in ensuring national food security, especially with Indonesia's population projected to reach 381 million by 2045. The implementation of precision agriculture supported by Unmanned Aerial Vehicle (UAV) technology offers an effective solution to improve agricultural production efficiency by accurately targeting pesticide distribution in areas where crops are experiencing stress (stressed regions). However, planning efficient UAV routes remains a major challenge in its application. This study aims to develop a Hybrid Genetic Algorithm - Variable Neighborhood Search (HGA-VNS) to optimize UAV routing in the context of precision agriculture. The methodology includes secondary data collection, clustering of stressed regions using the Lloyd algorithm, and route optimization using HGA-VNS. Experimental results show that the HGA-VNS algorithm improves route efficiency with an average travel distance of 1961.29 meters, outperforming the benchmark Google OR-Tools algorithm, which produced an average of 1970.48 meters. Therefore, the proposed HGA-VNS algorithm has the potential to be practically applied to support more efficient precision agriculture practices.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Precision agriculture, Unmanned Aerial Vehicle, Vehicle Routing Problem, Genetic Algorithm, Variable Neighborhood Search. |
| 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: | Nikolaus Vico Cristianto |
| Date Deposited: | 25 Jul 2025 07:51 |
| Last Modified: | 25 Jul 2025 07:51 |
| URI: | http://repository.its.ac.id/id/eprint/121822 |
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