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

 Amalia Hanifah Artya (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Jasril Jasril (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 (*)Suwanto Sanjaya Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Fadhilah Syafria (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Elvia Budianita (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

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.

Keywords


AlexNet; Convolutional Neural Network; Meat; RMSprop; SGD

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References

S. Wiryono, “Daging Sapi Oplosan Babi Ditemukan Beredar di Kota Tangerang,” 2020. https://megapolitan.kompas.com/read/2020/05/18/11430321/daging-sapi-oplosan-babi-ditemukan-beredar-di-kota-tangerang.

W. Pradana, “6 Tahun Beraksi, Suami-Istri Jual Daging Sapi Oplos Celeng ke Restoran,” 2020. https://news.detik.com/berita-jawa-barat/d-5074664/6-tahun-beraksi-suami-istri-jual-daging-sapi-oplos-celeng-ke-restoran?_ga=2.223217688.2082828355.1635324460-2111730206.1635324460.

M. F. Nurul Lihayati, Ratri Enggar Pawening, “Klasifikasi Jenis Daging Berdasarkan Tekstur Menggunakan Metode Gray Level Coocurent Matrix,” vol. 8, no. 1994, pp. 305–310, 2016.

L. N. A M Priyatno, F M Putra, P Cholidhazia, “Combination of extraction features based on texture and colour feature for beef and pork classification Combination of extraction features based on texture and colour feature for beef and pork classification,” 2020, doi: 10.1088/1742-6596/1563/1/012007.

J. Lana Maghfira, Muhammad Nasir, “Sistem Pendeteksi Kualitas Daging Berbasis Android,” vol. 3, no. 2, pp. 32–41, 2020.

C. A. S. Usman Sudibyo, Desi Purwanti Kusumaningrum, Eko Hari Rachmawanto, “Optimasi Algoritma Learning Vector Quantization (LVQ) Dalam Pengklaasifikasikan Citra Daging Sapi dan Daging Babi,” vol. 9, no. 1, pp. 1–10, 2018.

dan R. K. Mahmoud Al-Sarayreh, Marlon M. Reis, Wei Qi Yan, “Detection of Red-Meat Adulteration by Deep Spectral – Spatial Features in Hyperspectral Images,” 2018, doi: 10.3390/jimaging4050063.

S. Mutrofin et al., “Komparasi Kinerja Algoritma C4 . 5 , Gradient Boosting Trees, Random Forests, dan Deep Learning Pada Kasus Educational Data Mining,” vol. 7, no. 4, pp. 807–814, 2020, doi: 10.25126/jtiik.2020732665.

P. W. Pin Wang, En Fan, “Comparative Analysis of Image Classification Algorithms Based on Traditional Machine Learning and Deep Learning,” Pattern Recognit. Lett., 2020, doi: 10.1016/j.patrec.2020.07.042.

R. R. Nuli Giarsyani, Ahmad Fathan Hidayatullah, “Komparasi Algoritma Machine Learning dan Deep Learning Untuk Named Entity Recognition : Studi Kasus Data Kebencanaan,” vol. 3, no. 1, 2020.

A. R. K. Mahmoud Al-Sarayreh, Marlom M. Reis, Wei Qi Yan, “Deep Spectral-spatial Features of Snapshot Hyperspectral Images for Red-meat Classification,” 2018 Int. Conf. Image Vis. Comput. New Zeal., pp. 1–6.

dan B. S. Salasabila, Anwar Fitrianto, “Image Classification Modelling of Beef and Pork Using Convolutional Neural Network,” vol. 4531, pp. 26–38, 2021.

B. S. Salsabila, Anwar Fitrianto, “Image Classification of Beef and Pork Using Convolutional Neural Network in Keras Framework,” vol. 05, no. 02, pp. 5–8, 2021.

S. W. Nila Susila Yulianti, Kudang Boro Seminar, Joko Hermanianto, “Identifikasi kemurnian daging berbasis analisis citra,” vol. 8, no. 4, pp. 643–650, 2021, doi: 10.25126/jtiik.202183307.

A. S. Dicki Irfansyah, Metty Mustilasari, “Arsitektur Convolutional Neural Network ( CNN ) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi,” vol. 6, no. 2, pp. 87–92, 2021.

S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018.

H. Zhang, Z. Qu, and L. Yuan, “A Face Recognition Method Based on LBP Feature for CNN,” pp. 544–547, 2017.

J. Jasril and S. Sanjaya, “Learning Vector Quantization 3 (LVQ3) and Spatial Fuzzy C-Means (SFCM) for Beef and Pork Image Classification,” Indones. J. Artif. Intell. Data Min., vol. 1, no. 2, p. 60, 2018, doi: 10.24014/ijaidm.v1i2.5024.

M. Nasir, N. Suciati, and A. Y. Wijaya, “Kombinasi Fitur Tekstur Local Binary Pattern yang Invariant Terhadap Rotasi dengan Fitur Warna Berbasis Ruang Warna HSV untuk Temu Kembali Citra Kain Tradisional,” Inspir. J. Teknol. Inf. dan Komun., vol. 7, no. 1, 2017, doi: 10.35585/inspir.v7i1.2435.

Y. Ismail, I. P. N. Purnama, Sutardi, and L. B. Askara, “Pengenalan Wajah Berbasis Perhitungan Jarak Fitur LBP Menggunakan Euclidean, Manhattan, Chi Square Distance,” Semnastik, pp. 386–393, 2019.

A. Satyo et al., “Arsitektur Alexnet Convolution Neural Network ( CNN ) Untuk Mendeteksi Covid-19 Image Chest-Xray,” pp. 482–485, 2021.

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Copyright (c) 2022 Amalia Hanifah Artya, Jasril , Suwanto Sanjaya, Fadhilah Syafria, Elvia Budianita

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