Implementasi Convolutional Neural Network Untuk Klasifikasi Daging Menggunakan Fitur Ekstraksi Tekstur dan Arsitektur AlexNet

Abstract

The demand for meat began to increase rapidly, causing drastic price changes and causing the existence of scammers to inflate the price of meat to get big profits by mixing beef and pork. Few consumers are aware of the mixing of meat, to distinguish between beef and pork can be seen in terms of color and texture, but this theory still has weaknesses. This research uses the Deep Learning method, namely Convolutional Neural Network with Local Binary Pattern texture extraction feature and AlexNet architecture for meat classification. The research conducted stated that the accuracy of the meat image classification can be measured using various parameters and optimizers. The highest accuracy results obtained from this study were 68.6% accuracy, 62% precision, 57.6% recall, and 59% f1-score using the Stochastic Gradient Descent (SGD) optimizer, 0.01 learning rate, 32 batch size, and 0.9 momentum. Compared to the original dataset, the accuracy of the LBP dataset type is still below the original dataset with the results obtained from the accuracy of the original dataset are 84.1% accuracy, 78.6% precision, 79% recall, and 79% f1-score using the RMSprop optimizer, 0 .0001 learning rate, 32 batch sizes, and momentum So it can be concluded that the AlexNet architecture by setting the existing parameter values can increase the accuracy value.

Image Classification of Beef and Pork Using Convolutional Neural Network Architecture EfficienNet-B1

“…Similarly, research by Sarah, L. et al [16] employing CNN ResNet-50 produced average accuracy, recall, and precision values of 87.64%, 87.59%, and 90.90% [16]. Another study using CNN with AlexNet [17] resulted in an accuracy of 84.1%, precision of 78.6%, and recall of 79%. In addition, research by Alhafis G.Y et al [18] implemented CNN (EfficientNet-B0) with Contrast Limited Adaptive Histogram Equalization (CLAHE) obtained results of 95.17%, precision of 92.72% and recall of 95.5%.…”

Klasifikasi Citra Daging Sapi dan Babi Menggunakan CNN Alexnet dan Augmentasi Data

“…Terakhir, penelitian klasifikasi daging menggunakan fitur ekstraksi tekstur dan menggunakan arsitektur Alexnet. Penelitian ini berhasil mendapatkan akurasi tertinggi sebesar 85,3%, precision 87%, recall 76%, dan f1-score 81% [15].…”

“…Penelitian ini melanjutkan [15] karena hasil pengunaan ekstraksi tekstur masih belum memberikan peningkatan akurasi. Pada penelitian ini digunakan teknik augmentasi data untuk memperbaiki performa dan meningkatkan akurasi penelitian [16].…”

Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan CNN Arsitektur EfficientNet-B6 dan Augmentasi Data

“…Penelitian sebelumnya oleh Gusrifaris Yuda Alhafis mengenai klasifikasi citra daging sapi dan daging babi menggunakan metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dengan arsitektur EfficientNet-B0 menjadi dasar dari penelitian ini. Penggunaan metode CLAHE pada penelitian sebelumnya terbukti tidak memberikan peningkatan nilai akurasi yang diharapkan [21]. Oleh karena itu, dalam penelitian ini, penulis akan menggunakan augmentasi data dengan harapan dapat meningkatkan kinerja dan akurasi model [22].…”