IDENTIFIKASI KANKER KULIT (MELANOMA) PADA CITRA MENGGUNAKAN ALGORITMA MACHINE LEARNING K-NEAREST NEIGHBOR (K-NN) CLASSIFIER

Jasmine Athira Azzahra, . (2023) IDENTIFIKASI KANKER KULIT (MELANOMA) PADA CITRA MENGGUNAKAN ALGORITMA MACHINE LEARNING K-NEAREST NEIGHBOR (K-NN) CLASSIFIER. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Cancer is a condition in which our body's cells grow out of control and unnaturally. Cells that are found in almost all parts of the body can become cancerous and then spread to other areas of the body. Melanoma is a cancer that begins in melanocytes, which are the components that produce and distribute melanin in the skin. Most melanoma cells still make melanin, so melanoma tumors are usually brown or black like a mole. There are still many who do not realize the importance of checking the condition of the skin. Unknowingly, it turns out that many people suddenly feel pain and after being examined, it turns out that they have skin cancer (Melanoma). Starting from this problem, a system is needed to identify skin cancer. Therefore, this study proposes the application of machine learning in identifying the occurrence of Skin Cancer (Melanoma) using the K-Nearest Neighbor (K-NN) method with K=7 based on texture features in the image using the Local Binary Pattern feature extraction method using cross-validation. 5 folds with the first iteration folding results an accuracy value of 95%, the 2nd iteration results an accuracy value of 100%, the 3rd iteration results an accuracy value of 100%, the 4th iteration results an accuracy value of 55% and the 3rd iteration 5 results an accuracy value of 100% so that the average accuracy value using the K-Fold is 90%.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 1910511016] [Pembimbing: Iin Ernawati] [Penguji 1: Bambang Saras Yulistiawan] [Penguji 2: Nurhafifah Matondang]
Uncontrolled Keywords: Imagery, Machine Learning, K-Nearest Neighbor (K-NN), Skin Cancer, Melanoma, Classification, Local Binary Pattern
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Jasmine Athira Azzahra
Date Deposited: 28 Jul 2023 06:39
Last Modified: 28 Jul 2023 06:39
URI: http://repository.upnvj.ac.id/id/eprint/25219

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