Model Machine Learning Untuk Klasifikasi Kesegaran Daging Menggunakan Arsitektur Transfer Learning Xception
DOI:
https://doi.org/10.26418/justin.v11i2.57517Keywords:
Machine Learning, Xception, Transfer Learning, Klasifikasi, Kesegaran DagingAbstract
Peningkatan kebutuhan daging di Indonesia sering diikuti dengan permintaan daging yang meningkat di pasaran. Terjadinya peningkatan kebutuhan konsumsi ini seringkali menimbulkan kekhawatiran akan terjadinya pemalsuan dan pencampuran daging. Ketersediaan daging yang layak konsumsi dan berkualitas sangat dibutuhkan masyarakat. Sulitnya orang awam untuk mendeteksi tingkat kesegaran daging sering dimanfaatkan oleh oknum yang tidak bertanggung jawab untuk mendapatkan keuntungan. Adanya suatu sistem otomatis yang dapat mendeteksi kesegaran daging akan sangat membantu dalam mengatasi permasalahan sulitnya deteksi kesegaran daging. Kesegaran daging dapat dikategorikan antara lain daging segar, setengah segar dan tidak segar. Penggunaan model machine learning untuk mendeteksi kesegaran daging dapat membantu memecahkan permasalahan tersebut. Penelitian ini menggunakan pendekatan machine learning untuk melakukan klasifikasi kesegaran daging. Klasifikasi menggunakan arsitektur transfer learning Xception yang dapat meningkatkan performa model machine learning yang dihasilkan. Hasil evaluasi model didapatkan nilai accuracy 86,92%; precision 87,25%; recall 86,47%; dan f1 score 87,59%. Pengujian prediksi dengan menggunakan data baru juga berhasil memprediksi citra daging dengan baik. Hasil pengujian data menunjukkan model yang dihasilkan best-fitting untuk penggunaannya dalam klasifikasi tingkat kesegaran daging menggunakan dataset Meat Freshness.
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