Penerapan ResNet50-CNN untuk Optimalisasi Klasifikasi pada Data Fashion

Arimbi Puspitasari; Diana Sava Salsabila; Dwi Roliawati

Authors

Arimbi Puspitasari Universitas Islam Negeri Sunan Ampel Surabaya, Angola
Diana Sava Salsabila Universitas Islam Negeri Sunan Ampel Surabaya, Indonesia
Dwi Roliawati Universitas Islam Negeri Sunan Ampel Surabaya, Indonesia

Keywords:

Convolutional Neural Network (CNN), ResNet-50, Klasifikasi Gambar, Fashion

Abstract

Penelitian ini bertujuan untuk menerapkan arsitektur Convolutional Neural Network (CNN) berbasis ResNet-50 dalam klasifikasi gambar produk fashion. Model ini dikembangkan untuk mengenali berbagai kategori produk, seperti kaos, celana, dan sepatu, dengan memanfaatkan konsep residual learning yang memungkinkan jaringan mempelajari fitur visual kompleks secara lebih efektif. Metode penelitian meliputi pengumpulan dan pemrosesan data gambar, pelatihan model CNN menggunakan ResNet-50, serta evaluasi performa menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa model mencapai accuracy sebesar 99,44% pada data training dan 97,83% pada data testing, menunjukkan kemampuan generalisasi yang baik terhadap data yang belum pernah dilihat sebelumnya. Evaluasi lebih lanjut menggunakan confusion matrix menunjukkan bahwa sebagian besar sampel diklasifikasikan dengan benar, meskipun masih terdapat beberapa kesalahan prediksi pada kategori tertentu. Dengan rata-rata precision, recall, dan F1-score mencapai 98%, model ini terbukti memiliki performa tinggi dalam klasifikasi gambar fashion. Hasil penelitian ini menunjukkan bahwa ResNet-50 dapat menjadi solusi yang andal untuk sistem rekomendasi produk, katalog digital, dan pengelolaan inventaris berbasis gambar, meskipun masih terdapat ruang untuk peningkatan terutama pada kelas yang sulit diklasifikasikan

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