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All Journal Techno.Com: Jurnal Teknologi Informasi Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Informatika dan Rekayasa Perangkat Lunak BUDGETING : Journal of Business, Management and Accounting International Journal of Artificial Intelligence and Robotics (IJAIR) Journal of Soft Computing Exploration Jurnal Citrakara Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Seminar Nasional Teknologi dan Multidisiplin Ilmu
Ery Mintorini
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Transportation Other

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Journal : Journal of Soft Computing Exploration

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Breast tumor classification using adam and optuna model optimization based on CNN architecture Sari, Christy Atika; Rachmawanto, Eko Hari; Daniati, Erna; Setiawan, Fachruddin Ari; Hyperastuty, Agoes Santika; Mintorini, Ery
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.373

Abstract

Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.
Co-Authors Ainun Muhtadin Ali, Rabei Raad Angel Frilyaningrum Arika Norma Wahyu Dorroty Bonifacius Vicky Indriyono Budi Widjajanto Chandriana, Tri Dian Christy Atika Sari Dibyo Adi Wibowo Didik Hermanto Dimentieva, Imelda Eko Hari Rachmawanto Erika Devi Udayanti Erna Daniati Ferdianto , Bhekti Eka Gilang Rizky Gardianto Hyperastuty, Agoes Santika Ichwan Setiarso Ida Farida Imam Nuryanto Indriyono, Bonifacius Jaka Prasetya karmila karmila Lewa, Andi Hallang Mellati, Pita Moch. Sjamsul Moch. Sjamsul Hidajat Natalinda Pamungkas Pamungkas, Natalinda Pratama, Zudha Prayugo, Moh. Lambang Putri Hartarani, Wiedya Sanina Quamila Putri Setiawan, Aries Setiawan, Fachruddin Ari Tri Esti Rahayuningtyas Wibowo, Toni Widyatmoko Widyatmoko Wildan Mahmud Wisnu Pratama Zahari, Iqlima