Klasifikasi Jenis Ikan Cupang Menggunakan Convolutional Neural Network dengan Arsitektur ResNet-50
DOI:
https://doi.org/10.29407/meqqs611Keywords:
Klasifikasi, CNN, ResNet-50, CupangAbstract
Penelitian ini bertujuan untuk mengembangkan sistem identifikasi jenis ikan cupang menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur ResNet-50. Banyaknya variasi bentuk dan warna ikan cupang sering kali menyulitkan penggemar maupun pedagang dalam mengenali jenis ikan cupang secara akurat. Penelitian ini mengklasifikasikan tiga jenis ikan cupang, yaitu Halfmoon, Crowntail, dan Plakat. Model dilatih dengan pendekatan transfer learning dan dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa sistem mampu mengidentifikasi jenis ikan cupang dengan tingkat akurasi yang tinggi, di mana ikan cupang Halfmoon memperoleh akurasi tertinggi sebesar 99%, diikuti oleh Crowntail sebesar 97,52%, dan Plakat sebesar 95,68%. Perbandingan nilai akurasi dan loss pada data training dan validasi menunjukkan bahwa model tidak mengalami overfitting. Sistem ini diharapkan dapat membantu pengguna dalam mengidentifikasi jenis ikan cupang secara lebih mudah, cepat, dan akurat
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