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All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik CogITo Smart Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) JURTEKSI JOISIE (Journal Of Information Systems And Informatics Engineering) JATI (Jurnal Mahasiswa Teknik Informatika) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Manajemen Informatika Jayakarta J-SAKTI (Jurnal Sains Komputer dan Informatika) Edu Sociata : Jurnal Pendidikan Sosiologi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA
Safii, M.
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Journal : CogITo Smart Journal

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Hyperparameter Tuning Exploration to Maximize MobileNet Performance in Classification Kidney Tumor Siregar, Sandy Putra; Safii, M.; Andani, Sundari Retno
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.960.447-461

Abstract

The main focus in this research is how to develop the MobileNet architecture in order to produce a kidney tumor classification model that is accurate, resistant to overfitting, and remains consistent with variations in datasets and training parameters. This study aims to develop MobileNet architecture to produce a software model that can precisely identify with high accuracy, perform kidney tumor classification, and avoid failure in generalizing new data called overfitting, as well as evaluate the difference in accuracy generated from several variations of datasets and parameters. The method used in this study is MobileNet with hyperparameter tuning and fine-tuning, and it was compared with the MobileNet Baseline method. The dataset consists of 12,446 images classified as Normal, Cyst, Stone, and Tumor, collected from Kaggle. The findings of this study on the division of the 80:10:10 ratio of the proposed method image data resulted in 100% accuracy, 100% precision, 100% recall, and 100% F1-Score. This study is expected to produce architecture modifications that can classify kidney tumors with high accuracy so that the hypothesis is achieved. In addition, various approaches in medical image analysis using deep learning have shown better results in identifying various tumors, especially this research in the classification and detection of kidney tumors.
Co-Authors Alfina, Ommi Ardilla, Ririn Arfika, Retno Belli Nasution Butarbutar, Ehrlich F.T Cut Irna Liyana Dewi, Rafiqa Eka Irawan Fitri Anggraini, Fitri Hambali, Humaidi Hartama, Dedy Irfan Sudahri Damanik Kartina, Anggriani Dwi Khusnul Khatimah Kiswara, Qodrat Lahilote, Abqoriy Hisan Lubis, Nur Azizah Muhammad Ridwan Lubis Napitupulu, Pretty Natalia Nasution, Mhd Aditia Puspita, Dahlia Rafai, Muhammad Ramadani, Saputra Rawinda Fitrotul Mualafina, Rawinda Fitrotul Renaldi Renaldi Rizki, Fahrizal RUSTAM, MUHAMMAD TAUFIQ Sari, Andini Fadila Sari, Feby Widya Siallagan, Sri Anita SIREGAR, ELIDA TUTI Siregar, Sandy Putra Solikhun Solikhun, Solikhun Suhendro, Dedi Sundari Retno Andani Syahputri, Nita Syaputri, Vera Trisno, Agus Wanto, Anjar