Penerapan Arsitektur EfficientNet-B0 Pada Klasifikasi Leukimia Tipe Acute Lymphoblastik Leukimia
Abstract
Leukimia merupakan jenis kanker darah yang keganasannya dapat berkembang dengan cepat. Penundaan penanganan akan berakibat fatal dalam waktu beberapa bulan. Proses diagnosa dengan cepat dilakukan dengan cara memanfaatkan pemrosesan citra. Sebuah sistem dirancang agar mampu menglasifikasikan penyakit leukimia tipe Acute Lymphoblastic Leukemia (ALL) ke dalam 4 kelas, yaitu : Benign, Early, (Pre) Precursor, dan Pro (Progenitor) dengan memanfaatkan salah satu arsitetktur dari Convolutional Neural Network (CNN) yaitu EfficienNet-B0. Skenario pengujian dilakukan terhadap hyperparameter pada arsitektur EfficienNet-B0 melalui epoch (20, 30 dan 50) dengan learning rate (0.0001, 0.0003, 0.001, 0.003) dan optimizer jenis Adam dan RMSProp. Hasilnya adalah nilai performa akurasi pada data train mencapai 97,84% dan pada data test sebesar 98, 48%.
Keywords
References
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DOI: 10.30595/jrre.v6i1.22090
This work is licensed under a Creative Commons Attribution 4.0 International License.
ISSN: 2685-5313
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