Penyelesaian Set Covering Problem Pada Stressed Region Di Ladang Pertanian Menggunakan Differential Evolution Dan Ant Colony Optimization

Fudail, Muhammad Viggo Fudail (2026) Penyelesaian Set Covering Problem Pada Stressed Region Di Ladang Pertanian Menggunakan Differential Evolution Dan Ant Colony Optimization. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Precision agriculture merupakan pendekatan modern yang memanfaatkan teknologi seperti Unmaned Aerial Vehicle (UAV) dan analisis data spasial untuk meningkatkan efisiensi pengelolaan lahan pertanian, termasuk dalam penanganan tanaman yang mengalami stres. Permasalahan utama yang diangkat dalam penelitian ini adalah bagaimana memaksimalkan cakupan penanganan stressed region sekaligus meminimalkan tumpang tindih penggunaan sumber daya melalui metode optimasi yang tepat. Untuk menjawab permasalahan tersebut, penelitian ini menawarkan solusi berupa penerapan Set Covering Problem (SCP) untuk menentukan titik semprot optimal UAV menggunakan algoritma metaheuristik. Metodologi yang digunakan meliputi pengumpulan data lahan dan stressed region, pembentukan initial solution melalui K-Means, optimasi titik semprot menggunakan algoritma hibrid dari Differential Evolution dan Ant Colony Optimization, serta evaluasi titik semprot menggunakan OR-Tools. Hasil uji coba pada konfigurasi bobot terbaik menunjukkan bahwa solusi awal K-Means dengan coverage 95,24%, overlap 16,20%, dan 289 titik disempurnakan oleh Differential Evolution menjadi overlap 14,44%, lalu oleh Ant Colony Optimization menjadi overlap 9,28% dengan 266 titik dan coverage 95,02%, sehingga nilai loss menurun dari 330,41 menjadi 160,52. Perbandingan dengan OR-Tools menunjukkan jumlah titik hasil Ant Colony Optimization hanya berselisih tiga titik dari minimum teoretis, namun dengan tumpang tindih dua hingga tiga kali lipat lebih kecil. Melalui pendekatan ini, penelitian terbukti meningkatkan efisiensi penanganan area pertanian serta mendukung penerapan strategi pengelolaan sumber daya yang lebih tepat dan berkelanjutan.
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Precision agriculture has emerged as an essential approach to improving the efficiency and sustainability of modern agricultural practices through the use of spatial data analysis and autonomous systems. One of the key challenges in this domain is the effective handling of stressed region areas where crop conditions decline due to environmental factors, pests, or nutrient deficiencies. These regions require targeted treatment to avoid resource waste and ensure optimal crop health. This research addresses the problem of determining optimal spray coverage and flight routes for Unmanned Aerial Vehicles (UAVs) to maximize treatment coverage and minimize overlapping and unnecessary resource usage. To solve this issue, a Set Covering Problem (SCP) formulation is applied to generate initial spray points, followed by Differential Evolution (DE) and Ant Colony Optimization (ACO) to refine the point placement. The methodology includes data collection, clustering-based initial solution generation, optimization using DE and ACO, and point placement using OR-Tools. Experimental results under the best weight configuration show that the initial K-Means solution with 95.24% coverage, 16.20% overlap, and 289 points is refined by DE to 14.44% overlap, then by ACO to 9.28% overlap with 266 points and 95.02% coverage, reducing the loss value from 330.41 to 160.52. A comparison with OR-Tools shows that the ACO point count differs by only three points from the theoretical minimum, yet with two to three times lower overlap. Through this approach, the study demonstrates an efficient optimization framework that supports precise agricultural interventions, reduces input waste, and improves overall field management.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Precision Agriculture, UAV, Stressed Region, Set Covering Problem, Differential Evolution, Ant Colony Optimization
Subjects:	H Social Sciences > HE Transportation and Communications > HE336.R68 Route choice
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis
Depositing User:	Muhammad Viggo Fudail
Date Deposited:	29 Jul 2026 06:20
Last Modified:	29 Jul 2026 06:20
URI:	http://repository.its.ac.id/id/eprint/139440

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