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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Ilmiah Teknologi Informasi Asia SISFOTENIKA Jurnal Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Bina Insani ICT Journal Syntax Literate : Jurnal Ilmiah Indonesia Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISICOM (Journal of Information System, Infomatics and Computing) Technologia: Jurnal Ilmiah Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknologi Informatika dan Komputer Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Makara Journal of Technology SATIN - Sains dan Teknologi Informasi
Agus Salim
Universitas Bina Sarana Informatika
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Journal : SISFOTENIKA

 1 
Skin Cancer Classification Using Random Forest Algorithm Nurul Khasanah; Rachman Komarudin; Nurul Afni; Yana Iqbal Maulana; Agus Salim
SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/jst.v11i2.1122

Abstract

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.
Co-Authors Ade Nugraha Ali Haidir Andika Bayu Hasta Yanto Baginda Oloan Lubis Bibit Sudarsono Budi Santoso Chaidir Ishaq Crys Fajar Partana Daniati Uki Eka Saputri Dony Oscar Dwi, F Lia Dyah Anitasari, Dyah Eko Arif Riyanto, Eko Arif Firstianty Wahyuhening Fibriany Gamawanto, Rayhan Rahmat Aziz Ghofar Taufiq Hendri Hendri Irmawati Carolina Jaka Atmaja Jaka Atmaja Jefi Jefi Jefi Jefi Jefi Jefi Jefi, Jefi Juninisvianty, Tri Melan Susanti Mochammad Nizar Nurul Afni Nurul Khasanah Nurul Khasanah, Nurul Puspa Vicenna Barulah Yudha Rachman Komarudin Rahmat Tri Yunandar Rosiyadi, Didi Seimahuira, Syarah Suwardi Suwardi Yana Iqbal Yana Iqbal Maulana