Identifikasi Tumor Otak Menggunakan Jaringan Syaraf Tiruan Propagasi Balik pada Citra CT-Scan Otak
DOI:
https://doi.org/10.26418/pf.v5i3.22451Abstract
Telah dilakukan penelitian untuk mengidentifikasi tumor otak menggunakan jaringan syaraf tiruan dengan ekstraksi ciri Gray Level Co-Occurrence Matrix (GLCM). Penelitian ini menggunakan 10 citra otak normal dan 10 citra otak abnormal (tumor). Tahap preprocessing dimulai dengan memotong citra menjadi ukuran 256 x 256 piksel, kemudian dilanjutkan dengan mengubah citra berwarna (RGB) menjadi citra beraras keabuan (grayscale), proses histogram, dan ekualisasi histogram untuk memperbaiki kualitas tampilan citra. Proses selanjutnya menghitung ciri statistik menggunakan Gray Level Co-Occurrence Matrix (GLCM) 4 arah (0Ëš, 45Ëš, 90Ëš dan 135Ëš) dengan jarak d = 1. Fitur yang digunakan ada 4 yaitu kontras, korelasi, energi dan homogenitas. Identifikasi citra menggunakan jaringan syaraf tiruan propagasi balik dengan arsitektur [12 2 1]. Nilai Mean Square Error (MSE) antara target dan output jaringan saat pelatihan adalah 0,000253, sedangkan nilai MSE pada saat pengujian adalah 0,010688. Hasil penelitian menunjukkan bahwa propagasi balik dapat digunakan untuk mengidentifikasi citra otak normal dan citra otak abnormal (tumor) dengan tingkat akurasi sebesar 70%.
Kata Kunci : Citra Otak, GLCM, Jaringan Syaraf Tiruan, Propagasi Balik
References
Avenue BM. About Brain Tumors a Primer for Patients and Caregivers. Chicago: American Brain Tumor Association (ABTA); 2012.
Ferlay J, Shin H, Bray F, Forman D, Mathers C, Parkin D. International Agency for Research on Cancer. [Online].; 2010 [cited 2016 Maret. Available from: https://www.iarc.fr/en/media-centre/iarcnews/2010/globocan2008.php.
Bushberg J. The Essential Physics of Medical Imaging Philadephia: USA; 2003.
Susmikanti M. Pengenalan Pola Berbasis Jaringan Syaraf Tiruan dalam Analisa CT-Scan Tumor Otak Benigna. In Seminar National Aplikasi Teknologi Informasi 2010 (SNATI 2010); 2010; Yogyakarta.
Handayani N. Analisis Metode Jaringan Syaraf Tiruan Backpropagation untuk Pengenalan Sel Kanker Otak (Skripsi S1) Bandung: Universitas Komputer Indonesia; 2013.
Gadkari D. Image Quality Analysis using GLCM (Tesis S2) Orlando, Florida: University of Central Florida; 2004.
Nithya R, Santi B. Comparative Study on Feature Extraction Method for Breast Cancer Classification. Journal of Theoretical and Applied Information Technology. 2011 November; 33(2).
Listia R, Harjoko A. Klasifikasi Massa pada Citra Mammogram Berdasarkan Gray Level Cooccurrence Matrikx (GLCM). IJCCS. 2014 Januari; 8(1).
Hermawan A. Jaringan Syaraf Tiruan (Teori dan Aplikasi) Yogyakarta: Andi; 2006.
Anggriyani N. Klasifikasi Kanker Serviks Menggunakan Jaringan Syaraf Tiruan Backpropagation dengan Graphical User Interface (GUI) (Skripsi S1) Yogyakarta: Universitas Negeri Yogyakarta; 2015.
Downloads
Published
Issue
Section
License
Copyright Notice using Creative Commons License for an Online Journal
The online journal Prisma Fisika operates under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). This copyright notice serves to inform authors, readers, and users of the terms and conditions regarding the use and distribution of the journal's content. By submitting or accessing the content, you agree to the following:
1. Content License:
a. All articles, images, and supplementary materials published in Prisma Fisika are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
b. This license permits users to copy, distribute, and adapt the content for non-commercial purposes, provided appropriate credit is given to the original authors and the journal.
c. If you remix, transform, or build upon the content, you must distribute your contributions under the same license as the original.
2. Attribution:
a. When using or sharing content from Prisma Fisika, you must properly attribute the original authors and the journal.
b. Include the title of the article, the names of all authors, Prisma Fisika, the year of publication, the volume/issue number, and the specific page numbers.
c. Provide a hyperlink to the original article on Prisma Fisika's website, if applicable.
3. Non-Commercial Use:
a. The content published in Prisma Fisika may not be used for commercial purposes without obtaining explicit permission from the copyright holders.
b. Commercial use includes, but is not limited to, reproduction or distribution of the content for financial gain, selling or licensing the content, or using the content in products or services that are sold or monetized.
4. ShareAlike:
a. If you adapt or remix the content from Prisma Fisika, you must distribute your derivative work under the same Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
b. This provision ensures that any modifications, adaptations, or remixes of the content maintain the same open licensing framework.
5. Disclaimer:
a. The views expressed in articles published in Prisma Fisika are solely those of the authors and do not necessarily reflect the opinions or policies of the journal, its editorial board, or affiliated institutions.
b. Prisma Fisika and its editors are not liable for any errors, omissions, or damages arising from the use or interpretation of the published content.
Please note that this Copyright Notice does not supersede any additional permissions or restrictions that may be granted by individual authors or copyright holders for their specific content within Prisma Fisika. For any questions or requests regarding the use of the journal's content, please contact the editorial team at prismafisika@physics.untan.ac.id.
By accessing or contributing to Prisma Fisika, you agree to comply with the terms of this Creative Commons license. Violation of these terms may result in legal consequences as governed by applicable copyright laws.
Date: 24-05-2023
Prisma Fisika
Email: prismafisika@physics.untan.ac.id
