KOMPARASI AKURASI ARSITEKTUR MOBILENETV1 DAN RESNET MENGGUNAKAN META-LEARNING MENDETEKSI KUCING BERBASIS CLOUD COMPUTING

• FAIZ OCTA REYNALDI

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ABSTRAK

ABSTRAK

Faiz Octa Reynaldi (12190370). KOMPARASI AKURASI ARSITEKTUR MOBILENETV1 DAN RESNET MENGGUNAKAN META-LEARNING MENDETEKSI HEWAN KUCING BERBASIS CLOUD COMPUTING.

 

Object Detection memiliki beberapa kendala saat proses training seperti banyaknya data yang harus dilatih, menggunakan waktu cukup lama untuk dilatih dan lain-lain. Pada penelitian ini, penulis melakukan komparasi akurasi dan average loss training arsitektur SSD MobileNetV1 dan SSD ResNet menggunakan Pre-Trained model dengan metode Few-Shot Learning menggunakan Hold-Out Cross Validation untuk mendeteksi Objek Hewan Kucing Hitam dan Objek Hewan Kucing Putih dengan pengambilan data secara rill dari metode observasi Jakarta Vet Shop dan hanya membutuhkan sedikit data untuk dilakukannya proses training. Penelitian ini dilakukan dengan cara menggunakan Cloud Computing seperti Google Colab sebagai media untuk membandingkan akurasi arsitektur SSD MobileNetV1 dan SSD ResNet, Hasil analisa dalam penelitian ini adalah SSD ResNet memiliki akurasi yang tinggi dengan nilai rata-rata 100% pada kucing hitam dan nilai rata-rata 97.9% pada kucing putih sementara untuk SSD MobileNetV1 memiliki nilai rata-rata 99.66666667% pada kucing hitam dan 78.733% pada kucing putih. Kemudian SD MobileNetV1 memiliki Train Loss lebih besar dengan nilai rata-rata 0.003923 pada Kucing Hitam dan nilai rata-rata 0.0059 Kucing Putih jika dibandingkan dengan SSD ResNet dengan nilai rata-rata 0.030263 pada Kucing Hitam dan nilai rata-rata 0.00413 pada Kucing Putih.

 

Kata Kunci : Object Detection, Transfer Learning, Cloud Computing, Few-Shot  Learning, Hewan Kucing

 

KATA KUNCI

Analisis

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

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