Perbandingan Arsitektur MobileNetV2 dan RestNet50 untuk Klasifikasi Jenis Buah Kurma
DOI:
https://doi.org/10.26418/justin.v12i4.80358Keywords:
Klasifikasi, Kurma, MobileNetV2, RestNet50, Transfer LearningAbstract
Kurma adalah buah yang populer di Indonesia, terutama saat bulan Ramadhan karena mayoritas penduduknya beragama Islam. Buah ini berwarna coklat, berbentuk lonjong, dan tumbuh di pohon palem, serta kaya akan zat besi, kalsium, kalium, dan vitamin C. Kurma memiliki berbagai jenis dengan bentuk dan warna yang mirip, sehingga sulit diidentifikasi. Penelitian ini bertujuan untuk mengklasifikasikan jenis-jenis kurma menggunakan perbandingan arsitektur transfer learning. Metode yang digunakan adalah model CNN (Convolutional Neural Network) dengan arsitektur MobileNetV2 dan RestNet50, yang dilatih kembali menggunakan dataset citra untuk membedakan tiga jenis kurma: Ajwa, Alwassim, dan Khenaizi. Kedua model dilatih dengan parameter epoch 20, 40, dan 60. Hasil penelitian menunjukkan bahwa MobileNetV2 unggul dibandingkan RestNet50 dalam semua metrik evaluasi (accuracy, precision, recall, f1-score), dengan akurasi tertinggi 95% pada MobileNetV2. Hal ini mengindikasikan bahwa MobileNetV2 lebih efisien dalam memanfaatkan proses transfer learning dan lebih efektif dalam mengidentifikasi tiga jenis kurma pada dataset.References
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