Klasifikasi Ras Kucing Dengan Pendekatan Convolutional Neural Networks Menggunakan Arsitektur Inception V4

Adryan Putra Pratama; Jumadi Jumadi; Eva Nurlatifah

doi:10.32664/smatika.v15i02.1547

Penulis

Adryan Putra Pratama UIN Sunan Gunung Djati Bandung, Indonesia
Jumadi Jumadi UIN Sunan Gunung Djati Bandung, Indonesia
Eva Nurlatifah UIN Sunan Gunung Djati Bandung, Indonesia

DOI:

https://doi.org/10.32664/smatika.v15i02.1547

Kata Kunci:

Ras kucing, CNN, Inception V4, Pengenalan gambar, Klasifikasi

Abstrak

Klasifikasi ras kucing berdasarkan gambar menjadi tantangan karena perbedaan penampilan antar ras yang halus dan pengaruh lingkungan. Penelitian ini mengembangkan sistem klasifikasi otomatis berbasis arsitektur Inception V4 dengan pendekatan CRISP-DM, meliputi pemahaman bisnis, persiapan data, pemodelan, evaluasi, dan penerapan. Dataset yang digunakan berasal dari Oxford IIIT Pet Dataset, mencakup 12 ras kucing populer, dan diproses melalui pembersihan, augmentasi, normalisasi, serta pembagian menjadi data pelatihan (80%) dan validasi (20%). Model dilatih selama 25 epoch, mencapai akurasi validasi tertinggi 93.31%, dengan rata-rata precision, recall, dan f1-score sebesar 93%. Sistem ini diterapkan dalam aplikasi web berbasis Flask, memungkinkan klasifikasi real-time melalui unggahan gambar. Meskipun performa keseluruhan sangat baik, ras tertentu seperti Bengal menunjukkan peluang untuk perbaikan. Hasil ini menunjukkan bahwa model memiliki potensi besar untuk mendukung diagnosis kesehatan hewan peliharaan dan pelestarian ras. Penelitian ini memberikan kontribusi signifikan dalam pengembangan teknologi klasifikasi berbasis gambar, dengan saran untuk meningkatkan performa melalui augmentasi berbasis GAN dan pengujian pada dataset yang lebih beragam untuk generalisasi yang lebih baik.

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