Incremental Learning Pada Klasifikasi Penyakit Kulit Pada Citra Wajah

Asayanda, Fikra Agha Rabbani (2024) Incremental Learning Pada Klasifikasi Penyakit Kulit Pada Citra Wajah. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pada penelitian ini telah dilakukan pengembangan metode Incremental Learning untuk klasifikasi penyakit kulit pada citra wajah, Incremental Learning merupakan sebuah metode yang dapat menyesuaikan dengan data baru secara berkelanjutan tanpa melupakan informasi sebelumnya. dataset yang digunakan adalah penyakit kulit jerawat dan ham10000 dataset yang merupakan penyakit kulit untuk dilakukan klasfikasi pada model awal. Terdapat 1052 sampel untuk delapan class yang dilakukan untuk pelatihan data dengan akurasi testing sebesar 87% dan ditambahakan 800 data baru pada proses Incremental Learning menghasilkan akurasi sebe
sar 90% pada model yang diperbaharui.
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In this research, the Incremental Learning method has been developed for the classification of skin diseases on facial images. Incremental Learning is a method that can continuously adapt to new data without forgetting previous information. The datasets used include acne skin disease and the HAM10000 dataset, which are used for classification in the initial model. There were 1052 samples for eight classes used for training, resulting in a testing accuracy of 87%. Additionally, 800 new data points were added during the Incremental Learning process, achieving an accuracy of 90% in the updated model.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Incremental Learning, Klasifikasi, Dataset, Akurasi, Incremental Learning, Classification, Dataset, Accuracy.
Subjects:	R Medicine > R Medicine (General) > R858 Deep Learning R Medicine > RL Dermatology T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Computer Engineering > 90243-(S1) Undergraduate Thesis
Depositing User:	Fikra Agha Rabbani Asayanda
Date Deposited:	02 Aug 2024 04:25
Last Modified:	02 Aug 2024 04:25
URI:	http://repository.its.ac.id/id/eprint/110741

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