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All Journal ComEngApp : Computer Engineering and Applications Journal IPTEK The Journal for Technology and Science Fountain of Informatics Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Informatika Universitas Pamulang METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi INTEK: Informatika dan Teknologi Informasi Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Algoritme Jurnal Mahasiswa Teknik Informatika
Haq, Dina Zatusiva
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Transportation Other

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Journal : Jurnal Informatika Universitas Pamulang

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Pengolahan Citra Digital untuk Identifikasi Kanker Otak Menggunakan Metode Deep Belief Network (DBN) Muhammad Syaifulloh Fattah; Dina Zatusiva Haq; Dian Candra Rini Novitasari
Jurnal Informatika Universitas Pamulang Vol 6, No 4 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i4.13089

Abstract

The brain tumor is a dangerous disease for humans that can interfere with the functioning of the human brain. Brain tumors can develop into malignant brain tumors or brain cancer and cause death, so early detection is necessary to diagnose brain tumor disease. One way of early detection is to use the anatomy of an MRI scan of health images. The MRI scan results can diagnose patients, but it takes longer time. Therefore digital image processing is needed to facilitate an analysis so that it can be seen in the brain image there are tumor cells or not. In addition to digital image processing, a system that analyzes and detects data is also needed. The Deep Belief Network (DBN) method is used to identify data. This study conducted trials on the learning rate and network architecture. The results of the identification of brain cancer using the DBN method obtained a sensitivity (TP rate) value of 90.9%, a specificity (TN rate) of 100%, an accuracy of 95%, and a precision of 100% with a learning rate of 0.1 and using a 4-12-10-1 network architecture.
Co-Authors Abdillah, Rifqi Abdulloh Hamid Adzan, M. Sailul Andriyani , Mayrisa C. R. Novitasari, Dian Chastine Fatichah Hartanto, Salsabila Jannah, Rachma Raudhatul Muhammad Syaifulloh Fattah Muhtadin Billah, Muhammad Novitasari, Dian C Rini Nurwilda , Siti Pratama, Moch Deny Puspitasari, Wahyu Tri Putri Wulandari Ramadanti , Alvin Nuralif Rasyid Al-Hadi, Daud Sari, Dian Candra Rini Novita Sari, Firda Yunita Sholahuddin, Muhammad Zulfikar Sri Wahyuni Wulandari, A’isyah