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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK INFORMATIKA JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Teknologi Informasi dan Ilmu Komputer ICON-CSE Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Sriwijaya Journal of Informatics and Applications Jurnal Teknik Informatika (JUTIF) Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Abdimas Maduma Media Elektrik
Primanita, Anggina
Universitas Sriwijaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Other

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Klasifikasi Kanker Payudara Menggunakan Metode Convolutional Neural Network (CNN) dengan Arsitektur VGG-16 Idawati, Idawati; Rini, Dian Palupi; Primanita, Anggina; Saputra, Tommy
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 3 (2024): Maret 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7553

Abstract

Breast cancer classification is a process to determine the type and characteristics of breast cancer based on the characteristics of cancer cells. In this research, a system is designed to classify breast cancer using ultrasound images which are then processed using the Convolutional Neural Network method with the VGG-16 architecture. The aim of the research is to develop a breast cancer classification system using Convolutional Neural Network (CNN) and evaluate the classification results using Convolutional Neural Network (CNN) with the VGG-16 architecture. In breast cancer classification, three classes are considered: normal, benign, and malignant. The steps in the classification process include image input, filtering, resizing, data augmentation, and data digitization. The best results were obtained in this test using the SGD optimizer hyperparameter, learning rate 0.001, epoch 20 and batch size 32 producing an accuracy value of 78.87%, a precision value of 75.69%, an AUC value of 79.85% and an f1 score value of 74.67%.
Co-Authors Abdiansah Abdiansah Abdiansah, Abdiansah Abdurahman Abdurahman Ahmad Fali Oklilas Ahmad Nizar Ahmad Zarkasi Ananda Haykel Iskandar Athalaza, Muhammad Nawar Danny M. Saputra Darmawijoyo, Darmawijoyo Dedy Fitriady Fitriady Dian Palupi Rini Egga Asoka Ermatita - Fathoni Fathoni Frendredi Muliawan Hadi Purnawan Satria Hadi Purnawan Satria Hadipurnawan Satria Hadipurnawan Satria Hasby Rifky Idawati Idawati Isa, Indra Griha Tofik Iskandar, Ananda Haykel Jambak, Muhammad Ihsan M. Fachrurrozi . M. Nazhif Rizani Muhammad Afif Muhammad Fachrurrozi Muhammad Nawar Athalaza Muhammad Qurhanul Rizqie Muhammad Qurhanul Rizqie Muhammad Rasuandi Nugraha, Wibisena Osvari Arsalan Rafly Pakomgan Ramadhan, Muhammad Fajar Rizki Kurniati Rusdi Efendi Samsuryadi Samsuryadi Saparudin Saparudin Saputra, Tommy Sri Desy Siswanti Sutarno Sutarno Sutarno Sutarno Sutarno Syahroyni, Muhammad Syamsul Arifin Syamsuryadi Syamsuryadi Wibisena Nugraha