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language ind
format Thesis:Thesis
Thesis
PeerReview:NonPeerReviewed
PeerReview
Book:Book
Book
author AFRILIA, AFRILIA
Rini, Dian Palupi
Miraswan, Kanda Januar
title OPTIMASI FUNGSI KEANGGOTAAN FUZZY INFERENCE SYSTEM TSUKAMOTO DENGAN PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI CUACA DI PALEMBANG
publishDate 2019
isbn 0902118152002
topic T57-57.97 Applied mathematics. Quantitative methods
url http://repository.unsri.ac.id/5224/1/RAMA_55201_09021181520029_0023027804_0009019002_01_front_ref.pdf
http://repository.unsri.ac.id/5224/2/RAMA_55201_09021181520029_0023027804_0009019002_02.pdf
http://repository.unsri.ac.id/5224/3/RAMA_55201_09021181520029_0023027804_0009019002_05.pdf
http://repository.unsri.ac.id/5224/4/RAMA_55201_09021181520029_0023027804_0009019002_03.pdf
http://repository.unsri.ac.id/5224/5/RAMA_55201_09021181520029_0023027804_0009019002_04.pdf
http://repository.unsri.ac.id/5224/6/RAMA_55201_09021181520029_0023027804_0009019002_07_ref.pdf
http://repository.unsri.ac.id/5224/7/RAMA_55201_09021181520029_0023027804_0009019002_08_lamp.pdf
http://repository.unsri.ac.id/5224/8/RAMA_55201_09021181520029_0023027804_0009019002_06.pdf
http://repository.unsri.ac.id/5224/9/RAMA_55201_09021181520029.pdf
http://repository.unsri.ac.id/5224/10/RAMA_55201_09021181520029_TURNITIN.pdf
http://repository.unsri.ac.id/5224/
contents Fuzzy Inference System is one method that can be applied to predict the weather. However there is a problem that is often faced when implementing fuzzy logic, the problem is difficulty of determining the appropriate value limit of membership functions in a problem. Particle Swarm Optimization is one algorithm that is able to find optimal solutions because Particle Swarm Optimization focuses on solving optimization problems in the search for space to get solutions. So that the limits on the value of the membership function obtained can be an optimal solution. The result of weather prediction evaluation with Tsukamoto Fuzzy Inference System using the membership function obtained by Particle Swarm Optimization were able to improve the accuracy of weather prediction by 70,909% compared to using Tsukamoto Fuzzy Inference System with membership function of 50%.
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