Skin Cancer Classification Using Random Forest Algorithm

Sisfotenika

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Field Value
 
Title Skin Cancer Classification Using Random Forest Algorithm
 
Creator Khasanah, Nurul; UNIVERSITAS NUSA MANDIRI
Komarudin, Rachman; Universitas Nusa Mandiri
Afni, Nurul; Universitas Bina Sarana Informatika
Maulana, Yana Iqbal; Universitas Bina Sarana Informatika
Salim, Agus; Universitas Bina Sarana Informatika
 
Subject Skin Cancer; Random Forest Algorithm; Classification of Skin Cancer
 
Description Skin cancer is an excessive lump of skin tissue that affects the skin, has an irregular structure with cell differentiation at various levels in chromatin, nucleus and cytoplasm, is expansive, infiltrative to damage the surrounding tissue, and metastasizes through blood vessels and lymph vessels. Diagnosis of skin cancer by biopsy process is considered less effective because it costs a lot and can injure human skin as a sample. For that, we need a system for classification of skin cancer types that are effective and accurate. The application of machine learning has been widely used in the health sector. One of the machine learning methods is Random Forest. In this study, the histogram color feature extraction will be carried out, the hue moment shape extraction, and the haralick texture extraction. Furthermore, the image will be classified using the Random Forest algorithm. The best accuracy value obtained from the histogram feature extraction process and classification with Random Forest is 0.850822. The novelty of this research is the use of more diverse feature extraction and better accuracy results than previous studies. Future research is expected to use deep learning algorithms with CNN (Convolutional Neural Network) architecture to get better accuracy results and add application designs for the application of models that have been formed in the study so that they can be directly applied by the medical team.
 
Publisher STMIK PONTIANAK
 
Contributor
 
Date 2021-05-04
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://sisfotenika.stmikpontianak.ac.id/index.php/ST/article/view/1122
10.30700/jst.v11i2.1122
 
Source SISFOTENIKA; Vol 11, No 2 (2021): SISFOTENIKA; 137-147
SISFOTENIKA; Vol 11, No 2 (2021): SISFOTENIKA; 137-147
2460-5344
2087-7897
10.30700/jst.v11i2
 
Language ind
 
Relation http://sisfotenika.stmikpontianak.ac.id/index.php/ST/article/view/1122/748
 
Rights Copyright (c) 2021 SISFOTENIKA
http://creativecommons.org/licenses/by/4.0
 

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