Skin Cancer Classification Using Random Forest Algorithm


View Publication Info
Field Value
Title Skin Cancer Classification Using Random Forest Algorithm
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.
Date 2021-05-04
Type info:eu-repo/semantics/article

Format application/pdf
Source SISFOTENIKA; Vol 11, No 2 (2021): SISFOTENIKA; 137-147
SISFOTENIKA; Vol 11, No 2 (2021): SISFOTENIKA; 137-147
Language ind
Rights Copyright (c) 2021 SISFOTENIKA

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us


Copyright © 2015-2018 Simon Fraser University Library