Optimasi Rute Pemupukan Lahan Pertanian Menggunakan Genetics Algorithm dan Simulated Annealing

Al Karimi, Ziaul Haq (2024) Optimasi Rute Pemupukan Lahan Pertanian Menggunakan Genetics Algorithm dan Simulated Annealing. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Praktik pertanian modern menghadapi tantangan dari pertumbuhan populasi dan perubahan iklim, teknologi pertanian telah membantu mengurangi biaya operasional dan mengoptimalkan siklus produksi. Penelitian ini bertujuan merumuskan permasalahan pemupukan pada ladang pertanian dengan drone sesuai konsep Traveling Salesman Problem dan membangun Algoritma Genetics Algorithm dan Simulated Annealing untuk menyelesaikan permasalahan rute drone pada ladang penelitian. Tugas Akhir ini meliputi penggunaan Lloyd’s Algorithm untuk melakukan proses clustering dengan dua tipe yaitu tipe a dan tipe b, dan penentuan node pada lahan pertanian yang ada di Kelurahan Wonojoyo dan Kelurahan Brenggolo Kabupaten Kediri. Algoritma optimasi yang digunakan adalah Hybrid Genetics Algorithm dan Simulated Annealing. Uji Coba dilakukan dengan membandingkan hasil total waktu yang ditempuh drone pada initial solution Genetics Algorithm dan Hybrid Genetics Algorithm dan Simulated Annealing. Hasil penelitian menunjukkan bahwa algoritma Hybrid Genetics Algorithm dan Simulated Annealing menghasilkan pengurangan biaya rute sebesar 8,73% untuk lahan Sawah Wonojoyo dan 7,06% untuk lahan Sawah Brenggolo pada clustering tipe a. Pada clustering tipe b, menghasilkan pengurangan biaya rute sebesar 43,57%. untuk lahan Sawah Wonojoyo dan 10,69% untuk lahan Sawah Brenggolo. Hasil percobaan menunjukan bahwa pendekatan Hybrid Genetics Algorithm dan Simulated Annealing efektif dalam mengoptimalkan rute pemupukan pada lahan pertanian menggunakan drone.
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Modern agricultural practices face challenges from population growth and climate change, agricultural technology has helped reduce operational costs and optimize production cycles. This research aims to formulate the problem of fertilizing agricultural fields with drones according to the concept of Traveling Salesman Problem and build Genetics Algorithm and Simulated Annealing Algorithm to solve the problem of drone routes in the research field. This Final Project includes the use of Lloyd's Algorithm to perform the clustering process with two types, namely type a and type b, and the determination of nodes on agricultural land in Wonojoyo Village and Brenggolo Village, Kediri Regency. The optimization algorithm used is Hybrid Genetics Algorithm and Simulated Annealing. Trials were conducted by comparing the results of the total time traveled by the drone on the initial solution Genetics Algorithm and Hybrid Genetics Algorithm and Simulated Annealing. The results showed that the Hybrid Genetics Algorithm and Simulated Annealing algorithm resulted in a route cost reduction of 8.73% for Wonojoyo Rice fields and 7.06% for Brenggolo Rice fields in clustering type a. In clustering type b, it resulted in a route cost reduction of 43.57%. for Wonojoyo Rice fields and 10.69% for Brenggolo Rice fields. The experimental results show that the Hybrid Genetics Algorithm and Simulated Annealing approach is effective in optimizing fertilization routes on agricultural land using drones.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Optimasi Rute Lahan Pertanian, Drone, Genetics Algortihm, Simulated Annealing, Farm Route Optimization, Drones, Genetics Algorithm, Simulated Annealing.
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:	Ziaul Haq Al Karimi
Date Deposited:	02 Aug 2024 06:30
Last Modified:	02 Aug 2024 06:30
URI:	http://repository.its.ac.id/id/eprint/111479

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