OPTIMASI PENERAPAN ALGORITMA CONVOLUTION NEURAL NETWORK DALAM KLASIFIKASI TINGKAT KESEGARAN DAGING SAPI

  • Charisa Nur Sahera Universitas Muhammadiyah Sidoarjo
  • Yunianita Rahmawati Universitas Muhammadiyah Sidoarjo
  • Rohman Dijaya Universitas Muhammadiyah Sidoarjo

Abstract

This research discusses optimizing the application of the Convolutional Neural Network (CNN) algorithm to overcome the problem of mixing fresh and non-fresh beef on the market. The focus of the research is classification of freshness levels in beef images using the CNN method with ADAM optimizer. Data collection involves open and private data. Image preprocessing is done with LabelEncoder and cv2. The research results show that this method is very effective in identifying and classifying the level of freshness in beef images. By determining optimal parameters, the model achieved the highest accuracy level of 98.50% at 10 epochs and a learning rate value of 0.001. Confusion matrix shows good results with a high number of True Positives. By applying CNN with the ADAM optimizer, it provides an effective solution to the problem of classifying beef freshness levels because this model is able to classify beef images well.

References

[1] A. N. Rahmad and F. S. Pribadi, “Edu Komputika Journal,” Edu Komputika J., vol. 5, no. 1, pp. 33–43, 2018.
[2] A. Aulia, “Faktor–Faktor yang Mempengaruhi Harga Daging Sapi di Kota Banda Aceh.” UIN AR-RANIRY, 2021.
[3] S. Maiyena and E. R. Mawarnis, “Kajian analisis konsumsi daging sapi dan daging babi ditinjau dari kesehatan,” J. Pendidik. Tambusai, vol. 6, no. 1, pp. 3131–3136, 2022.
[4] E. Soesetyaningsih and A. Azizah, “Akurasi perhitungan bakteri pada daging sapi menggunakan metode hitung cawan,” Berk. sainstek, vol. 8, no. 3, pp. 75–79, 2020.
[5] A. Baiq Annisa Sulistia, “Pengaruh Lama Penyimpanan Dalam Freezer Terhadap Sifat Fisik dan Jumlah Bakteri pada Daging Sapi Bali Jantan.” Universitas Mataram, 2023.
[6] T. Yulianti, M. Telaumbanua, H. D. Septama, and H. Fitriawan, “The Effect Of Image Feature Selection On The Local Beef,” J. Tek. Pertan. Lampung, vol. 10, no. 1, pp. 85–95, 2021,
[7] Y. Pratama, E. Rasywir, F. Fachruddin, D. Kisbianty, and B. Irawan, “Eksperimen Layer Pooling menggunakan Standar Deviasi untuk Klasifikasi Dataset Citra Wajah dengan Metode CNN,” Build. Informatics, Technol. Sci., vol. 5, no. 1, pp. 200–210, 2023.
[8] I. A. Dly, S. Sanjaya, L. Handayani, and F. Yanto, “Klasifikasi Citra Daging Sapi dan Babi Menggunakan CNN Alexnet dan Augmentasi Data,” vol. 4, no. 4, pp. 1176–1185, 2023, doi: 10.47065/josh.v4i4.3702.
[9] M. F. Naufal et al., “Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19,” Teknika, vol. 10, no. 2, pp. 96–103, 2021, doi: 10.34148/teknika.v10i2.331.
[10] A. Deshpande, V. V. Estrela, and P. Patavardhan, “The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution, convolutional neural network, and the ResNet50,” Neurosci. Informatics, vol. 1, no. 4, p. 100013, 2021, doi: 10.1016/j.neuri.2021.100013.
[11] P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network (Cnn) Pada Ekspresi Manusia,” Algor, vol. 2, no. 1, pp. 12–20, 2020.
[12] H. Herimanto, “Perbandingan Matriks Loss Pada Model Deep Learning Resnet50 dan Xception dalam Deteksi Objek,” J. MEDIA Inform. BUDIDARMA, vol. 7, no. 4, pp. 1994–2002, 2023.
[13] D. Alamsyah and D. Pratama, “Implementasi Convolutional Neural Networks (CNN) untuk Klasifikasi Ekspresi Citra Wajah pada FER-2013 Dataset,” J. Teknol. Inf., vol. 4, no. 2, pp. 350–355, 2020.
[14] M. Kamal Hasan, Adiwijaya, and A. F. Said, “Klasifikasi Citra Multi-Kelas Menggunakan Convolutional Neural Network,” e-Proceeding Eng., vol. 6, no. 1, pp. 2127–2136, 2019.
[15] D. Irfansyah, M. Mustikasari, and A. Suroso, “Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi,” J. Inform. J. Pengemb. IT, vol. 6, no. 2, pp. 87–92, 2021.
[16] J. K. Azhar, “Optimalisasi Adaptive Karnel Convolution Neural Netwok Menggunakan Algoritma Adgrad.” Universitas Siliwangi, 2022.
[17] Y. Hartiwi, E. Rasywir, Y. Pratama, and P. A. Jusia, “Eksperimen Pengenalan Wajah dengan fitur Indoor Positioning System menggunakan Algoritma CNN,” J. Khatulistiwa Inform., vol. 22, no. 2, pp. 109–116, 2020.
[18] M. F. S. D. Cahyo and D. Udjulawa, “Identifikasi Daging Segar Berdasarkan Citra menggunakan Convolutional Neural Network,” in MDP Student Conference, 2023, pp. 306–313.
[19] K. B. V. Putra, I. P. A. Bayupati, and D. M. S. Arsa, “Klasifikasi Citra Daging Menggunakan Deep Learning dengan Optimisasi Hard Voting,” J. RESTI (Rekayasa Sist. Dan Teknol. Informasi), vol. 5, no. 4, pp. 656–662, 2021.
[20] E. Rasywir, R. Sinaga, and Y. Pratama, “Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN),” J. Parad. Ubsi, vol. 22, no. 2, pp. 117–123, 2020.
Published
2024-06-30
How to Cite
SAHERA, Charisa Nur; RAHMAWATI, Yunianita; DIJAYA, Rohman. OPTIMASI PENERAPAN ALGORITMA CONVOLUTION NEURAL NETWORK DALAM KLASIFIKASI TINGKAT KESEGARAN DAGING SAPI. Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 1, p. 1-8, june 2024. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1122>. Date accessed: 21 july 2024. doi: https://doi.org/10.37600/tekinkom.v7i1.1122.
Section
Articles