ANALISIS DAMPAK TRANSFER LEARNING PADA SEGMENTASI SEMANTIK CITRA HEWAN MENGGUNAKAN U-NET
Keywords:
convolutional neural network, deep learning, pengolahan citra, segmentasi sematik, transfer learning, u-netAbstract
Segmentasi merupakan salah satu metode pengolahan citra yang membagi citra menjadi beberapa segmen. Segmentasi pada percobaan ini menggunakan metode deep learning, yaitu convolutional neural network (CNN) dengan arsitektur U-Net. CNN adalah salah satu metode deep learning yang sangat populer pada pengolahan citra dan arsitektur U-Net biasa digunakan untuk segmentasi citra berjenis semantik. Hasil segmentasi dianalisa menggunakan metrik Intersection-over-Union (IoU) menunjukkan bahwa model U-Net dengan transfer learning memiliki performa yang lebih baik dibandingkan dengan model U-Net tanpa transfer learning.
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Copyright (c) 2022 Michael Stephen Lui, Michael David, Muhammad Rafif Al Aziz, Novanto Yudistira
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