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All Journal Jurnal Simetris INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal Locus Penelitian dan Pengabdian Scientific Journal of Informatics
Soeleman, M. Arief
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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Enhancing Monkeypox Skin Lesion Classification With Resnet50v2: The Impact Of Pre-Trained Models From Medical And General Domains Azhar, Saifulloh; Syukur, Abdul; Soeleman, M. Arief; Affandy, Affandy; Marjuni, Aris
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4486

Abstract

The monkeypox outbreak has emerged as a pressing global health concern, as evidenced by the rising number of cases reported in various countries. This rare zoonotic disease, caused by the Monkeypox virus (MPXV) of the Poxviridae family, is commonly found in Africa. However, since 2022, cases have also spread to various countries, including Indonesia. The dermatological symptoms exhibited by affected individuals vary, with the potential for further transmission through contamination. Early and accurate detection of monkey pox disease is therefore essential for effective treatment. The present study aims to improve the classification of Monkey Pox using the modified Resnet50V2 model, trained using pre-training datasets namely ImageNet and HAM10000, where batch size and learning rate parameters were adjusted. The study achieved high accuracy in distinguishing monkeypox cases, with 98.43% accuracy for Resnet50V2 with pretrained ImageNet and 70.57% accuracy for Resnet50V2 with pretrained HAM10000. Future research will focus on refining these models, exploring hybrid approaches incorporating convolutional neural networks, this advancement contributes to the development of automated early diagnosis tools for monkeypox skin conditions, especially in resource-limited clinical settings where access to dermatology experts is limited.
Co-Authors Abdul Syukur Abi Senoprabowo Affandy Affandy Affandy Al zami, Farrikh Assaqty, Mohammad Iqbal Saryuddin Azhar, Saifulloh Casidi, Casidi Dliyauddin, Muhammad Elkaf Rahmawan Pramudya Guruh Fajar Shidik Marjuni, Aris Muslih - Nurhindarto, Aris Prasetya, Rudy Eko Pulung Nurtantio Andono Rahmatullah, Muhammad Rifqi Fadhlan Sari Ayu Wulandari Supriyono Asfawi Triyotomo, Triyotomo Tutik Khotimah Zami , Farrikh Al