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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) International Journal of Advances in Data and Information Systems Computer Science and Information Technologies Jurnal Ilmiah Matematika
Aris Thobirin, Aris
University of Ahmad Dahlan
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Application of EfficientNetV2-S Architecture with Focal Loss to Overcome Class Imbalances in Skin Cancer Classification Wati, Marfungah; Thobirin, Aris; Surono, Sugiyarto
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1524

Abstract

Imbalanced class distributions in skin lesion image datasets can reduce the effectiveness of multiclass classification models. This research proposes a classification model based on the EfficientNetV2-S architecture with the application of two-stage training and loss functions that emphasize learning in classes with limited data. The models were trained using on-the-fly image augmentation and evaluated to assess generalization capabilities to the test data. In the initial stage, the model is trained by freezing the backbone and only updating the classifier layer. Next, fine-tuning was carried out on part of the backbone layer to adjust the representation of features to the image characteristics of the skin lesion. Evaluation is conducted through multiple training times with different random initializations to ensure consistency of results. The test results showed that the model experienced an improvement in performance after the fine-tuning process, with an accuracy of about 88% as well as an increase in F1-score values in some classes. Overall, the results indicate that the proposed approach may help improve classification performance when dealing with imbalanced skin cancer image data.
Co-Authors Abdillah, Alfiyyah 'Ainul Abdulnazar, Mohamed Naeem Antharathara Damayanti, Fanita Eko Prasetyo Firdausy, Salma Nadya Hanifa, Riza Aulia Ibad, Irsyadul Ida Ayu Putu Sri Widnyani Mujhid, Almuzhidul Parinzka, Zellya Rahmadani, Lanova Ade Rismawati, Dewi Salsabila, Sona Regina Subrata, Arsyad Cahya Sugiyarto Surono, Sugiyarto Suris, Badzliana Aqmar Surono , Sugiyarto Wati, Marfungah Wijayanti, Dian Eka