Analisis Hyperparameter Pada Klasifikasi Jenis Daging Menggunakan Algoritma Convolutional Neural Network

I Made Anom Mahartha Dinata(1*), I Gede Aris Gunadi(2), I Made Gede Sunarya(3),

(1) Universitas Pendidikan Ganesha, Indonesia
(2) Universitas Pendidikan Ganesha, Indonesia
(3) Universitas Pendidikan Ganesha, Indonesia
(*) Corresponding Author


In the context of food and economy, meat plays a vital role in fulfilling the nutritional needs of society and serves as a strategic economic commodity. However, the difficulty in distinguishing between beef and pork often leads to fraud by meat traders. Particularly in Indonesia, where the consumption of beef and pork is high, this confusion raises significant concerns, especially since pork is prohibited in the Islamic religion. This research aims to address this issue by applying Artificial Intelligence technology, specifically the Convolutional Neural Network (CNN) deep learning method in classifying images of beef, pork, and mixed meat. The study utilizes a dataset of 410 samples, with 70% used for training and 30% for testing. Testing is conducted using a basic CNN model with hyperparameter analysis such as image size, number of epochs, and batch size. Additionally, the dataset is tested using a comparative architecture, namely the ResNet-50 architecture. The best accuracy rate of the CNN model is 82.20%, achieved with an image size of 75 x 75 pixels, 100 epochs, and a batch size of 64. Testing with the ResNet-50 architecture yields the highest accuracy of 76.14%. Evaluation is performed using a confusion matrix with four categories: Accuracy, Precision, Recall, and F1 Score.

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S. Susanti, I. Isnawati, and F. I. Muhaimin, ‘Pengurangan Konsumsi Daging Merah Berlebih untuk Menghambat Penuaan’, Muhammadiyah Journal of Geriatric, vol. 3, no. 1, p. 17, 2022, doi: 10.24853/mujg.3.1.17-22.

Direktorat Jenderal Peternakan dan Kesehatan Hewan, ‘Membedakan Jenis Daging’, 2021. Accessed: Nov. 26, 2023. [Online]. Available:

T. Hidayat, F. Aziz, and D. U. E. Saputri, ‘Meat Image Classification Using Deep Learning With Resnet152V2 Architecture’, Jurnal Techno Nusa Mandiri, vol. 19, no. 2, pp. 131–140, 2022, doi: 10.33480/techno.v19i2.3932.

F. F. Maulana and N. Rochmawati, ‘Klasifikasi Citra Buah Menggunakan Convolutional Neural Network’, Journal of Informatics and Computer Science (JINACS), vol. 1, no. 02, pp. 104–108, 2020, doi: 10.26740/jinacs.v1n02.p104-108.

F. N. Cahya, N. Hardi, D. Riana, and S. Hadiyanti, ‘Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network (CNN)’, Sistemasi, vol. 10, no. 3, p. 618, 2021, doi: 10.32520/stmsi.v10i3.1248.

P. Winardi and E. Setyati, ‘Identifikasi Jenis Daging dengan Menggunakan Algoritma Convolution Neural Network’, Journal of Information System,Graphics, Hospitality and Technology, vol. 3, no. 02, pp. 82–88, Dec. 2021, doi: 10.37823/insight.v3i02.178.

A. Arif Budiman, L. Nur Afifa, T. Setiyaningsih, and T. Amin Ridho, ‘Membangun Model Pengidentifikasi Kesegaran Daging dengan Metode Jaringan Syaraf Konvolusi (CNN) Jenis Resnet-50’, vol. 7, p. 113, 2023, doi: 10.37817/ikraith-informatika.v7i3.

S. Lasniari, S. Sanjaya, F. Yanto, and M. Affandes, ‘Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi’, Universitas Islam Negeri Sultan Syarif Kasim Riau Jl. H.R Soebrantas No. 155 KM, vol. 5, no. 3, p. 28293, 2022



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