Analisis Performa CNN ResNet50 untuk Klasifikasi Penyakit Kulit Kepala Berbasis Digital

Authors

  • MOCHAMAD RISQI ANDRIANSYAH Universitas Nusantara PGRI Kediri Indonesia
  • Made Ayu Dusea Widyadara Universitas Nusantara PGRI Kediri Indonesia
  • Daniel Swanjaya Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/9y7bhq96

Abstract

Penyakit kulit kepala memiliki karakteristik visual yang mirip antar kelas sehingga proses identifikasi secara manual sering mengalami kesulitan. Penelitian ini bertujuan untuk menganalisis performa arsitektur Convolutional Neural Network (CNN) ResNet50 dalam klasifikasi penyakit kulit kepala berbasis citra digital. Dataset yang digunakan terdiri dari 12.000 citra dengan 10 kategori penyakit kulit kepala. Sebelum pelatihan dilakukan, citra melalui tahap preprocessing dan augmentasi data. Model dikembangkan menggunakan pendekatan transfer learning dan dievaluasi menggunakan accuracy, precision, recall, F1-score, dan confusion matrix. Hasil penelitian menunjukkan bahwa model mampu mencapai accuracy sebesar 98,75% dengan performa klasifikasi yang baik pada sebagian besar kelas. Hasil tersebut menunjukkan bahwa ResNet50 efektif digunakan untuk klasifikasi penyakit kulit kepala berbasis citra digital.

Keywords:

CNN, citra digital, klasifikasi citra, penyakit kulit kepala, ResNet50, transfer learning

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Published

2026-07-20

Issue

Vol. 10 No. 1 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026

Section

Articles

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How to Cite

Analisis Performa CNN ResNet50 untuk Klasifikasi Penyakit Kulit Kepala Berbasis Digital. (2026). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 10(1), 892-898. https://doi.org/10.29407/9y7bhq96