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All Journal JURNAL ILMIAH INFORMATIKA STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Journal of Informatics and Advanced Computing
Ionia Veritawati
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Analisis Gambar MRI Otak Untuk Mendeteksi Tumor Otak Menggunakan Algoritma CNN: Analisis Gambar MRI Otak Untuk Mendeteksi Tumor Otak Menggunakan Algoritma CNN Valliant Benvenuto Gianzurriell; Husnal, Ferdi; Fiky Ari Wijaya; Fahmi Fauzi; Iman Paryudi; Ionia Veritawati; Sri Rezeki Candra Nursari
Journal of Informatics and Advanced Computing (JIAC) Vol 4 No 2 (2023): Journal of Informatics and Advanced Computing (JIAC)
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/jiac.v4i2.6289

Abstract

Brain tumor disease is one of the deadliest diseases that can attack anyone without exception. This disease is characterized by the development of abnormal cells in human brain tissue is a sign of this disease. A digital image technology called Magnetic Resonance Imaging (MRI) can be used to detect these brain tumors. This technology is meant to help doctors identify and classify different types of brain tumors. An effective and accurate method is needed to perform MRI image classification as manual classification takes a long time and carries a high risk. One effective solution to this problem is Convolutional Neural Network (CNN). CNN is an algorithm that can learn itself from previous cases. The deep learning method, CNN with the VGG16 model, can be implemented as a solution to the problem. The process of making this system with the stages of making Image Detection, namely image acquisition, preprocessing, extraction, classification, and identification of image data. This study uses 3 datasets where each dataset has 1311 images of patient MRI results. The dataset is separated into 3 different data, namely train data, validation data, and test data. The results of testing these three datasets are able to identify the images tested into the system with a percentage accuracy of 99%.
Co-Authors Annisa Cintakami Firdaus Fahmi Fauzi Farah Husna Qani’ah Farsya Asfian Saputra Febri Maspiyanti Ferdy Dwiansyah Fiky Ari Wijaya Husnal, Ferdi Iman Paryudi Jhon Roy H. Siagian Murty, Filia Sekar putri, shabrina Rafiq Ramadhan Riadika Mastra Steffi Nova Ulfa Azizah Valliant Benvenuto Gianzurriell Yohanes Paulus Pical Zyhan Faradilla