KLASIFIKASI KANKER KULIT MELANOMA DENGAN PENGKLASIFIKASI BACKPROPAGATION NEURAL NETWORK

Pandu Ananto Hogantara, . (2021) KLASIFIKASI KANKER KULIT MELANOMA DENGAN PENGKLASIFIKASI BACKPROPAGATION NEURAL NETWORK. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Melanoma skin cancer is the deadliest type of skin cancer in the world. Melanoma skin cancer is almost impossible to cure when it has reached the final stages of cancer. Meanwhile, it is still curable in the early stages of the cancer. However, distinguishing benign lesions from melanoma cancer is hard because both have similar characteristics in the early stages of the development. The difficulty of distinguishing between benign lesions and melanoma cancer raises problems, namely underdiagnosis and overdiagnosis. Underdiagnosis is a condition in which melanoma is classified as a benign lesion, while overdiagnosis is a condition where a benign lesion is classified as a melanoma. Based on these problems, it is important to create an effective and accurate method that can classify melanoma cancer. This study uses the Gray Level Run Length Matrix (GLRLM) texture, HSV image color features, and shape features with backpropagation neural network to classify melanoma cancer. Based on this research, the color feature gets the best accuracy, sensitivity, and specificity among the three features, namely 77.33%, 80.33%, 74.33%. Meanwhile, the best overall accuracy was obtained when the three features were combined with the accuracy, sensitivity, and specificity score of 81.17%, 77.67%, 84.67%.

Item Type:	Thesis (Skripsi)
Additional Information:	[No. Panggil: 1710511034] [Pembimbing 1: Didit Widiyanto] [Pembimbing 2: Mayanda Mega Santoni] [Penguji 1: Iin Ernawati] [Penguji 2: Nurul Chamidah]
Uncontrolled Keywords:	melanoma, Gray Level Run Length Matrix, color histogram HSV, shape, backpropagation neural network
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User:	Pandu Ananto Hogantara
Date Deposited:	21 Dec 2021 07:51
Last Modified:	21 Dec 2021 07:51
URI:	http://repository.upnvj.ac.id/id/eprint/11104

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