PERBANDINGAN OPTIMIZER, BATCH SIZE DAN EPOCH PADA METODE CONVOLUTION NEURAL NETWORK
DOI:
https://doi.org/10.30656/jsii.v11i2.9249Abstract
Buffalo meat and beef are two types of red meat that are widely consumed by the public. The demand for meat increases every year. However, not all types of meat can be eaten by Indonesians, such as pork, so the price of pork in Indonesia is lower than the price of beef and buffalo. In general, the texture and colour of pork, beef and buffalo are almost the same. In the introduction of meat, it is only done directly from the colour, texture, and fibre of the type of meat. However, meat circulating in the community is often mixed between beef, buffalo meat and pork. Distinguishing beef, buffalo and pork must first recognise the characteristics of each type of meat, because there are limitations to the human sense of sight in distinguishing between them. In the use of technology with the help of digital images to determine the most optimal optimizer, batch size and epoch in meat classification, using the Convolutional Neural Network (CNN) method with NasNetmobil Architecture. The data set used is 3000 images divided into three classes, with a division of 2400 training data images, 300 testing data images, 300 validation data images. The results showed that the Adam optimiser, batch size 62 and epoch 20 produced an accuracy of 99.00% and a loss value of 0.0243.
Keywords: Convolutional Neural Network, Buffalo and Beef Classification,
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Alhafis, G. Y., Jasril, J., Sanjaya, S., Syafria, F., & Budianita, E. (2022). Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan Ekstraksi Ciri dan Convolutional Neural Network. JURIKOM (Jurnal Riset Komputer), 9(3), 653. https://doi.org/10.30865/jurikom.v9i3.4175
(2) Anisyah, A. F. (2021). Cerita Anggota DPR Temukan Daging Sapi Oplosan Jelang Lebaran 2021. merdeka.com. https://www.merdeka.com/uang/cerita-anggota-dpr-temukan-daging-sapi-oplosan-jelang-lebaran-2021.html
(3) Anwar, K. (2016). Pemilihan Fitur Berdasarkan Minimum Overlap Probability ( MOP ) dalam Mengidentifikasi Daging Sapi dan Daging Babi. 316–327.
(4) Asmara, R. A., Romario, R., Batubulan, K. S., Rohadi, E., Siradjuddin, I., Ronilaya, F., Ariyanto, R., Rahmad, C., & Rahutomo, F. (2018). Classification of pork and beef meat images using extraction of color and texture feature by Grey Level Co-Occurrence Matrix method. IOP Conference Series: Materials Science and Engineering, 434(1), 0–10. https://doi.org/10.1088/1757-899X/434/1/012072
(5) Darmanto, H. (2019). Pengenalan Spesies Ikan Berdasarkan Kontur Otolith Menggunakan Convolutional Neural Network. Joined Journal (Journal of Informatics Education), 2(1), 41. https://doi.org/10.31331/joined.v2i1.847
(6) Efendi, D., Jasril, J., Sanjaya, S., Syafria, F., & Budianita, E. (2022). Penerapan Algoritma Convolutional Neural Network Arsitektur ResNet-50 untuk Klasifikasi Citra Daging Sapi dan Babi. JURIKOM (Jurnal Riset Komputer), 9(3), 607. https://doi.org/10.30865/jurikom.v9i3.4176
(7) Ghosh, A., Sufian, A., Sultana, F., Chakrabarti, A., & De, D. (2019). Fundamental concepts of convolutional neural network. In Intelligent Systems Reference Library (Vol. 172, Nomor June). https://doi.org/10.1007/978-3-030-32644-9_36
(8) Hartono, A., Suendri, S., & Harahap, A. M. (2023). Penggunaan Algoritma Extreme Learning Machine Berbasis Android Untuk Mengidentifikasi Daging Sapi dan Babi. J-SISKO TECH (Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD), 6(1), 127. https://doi.org/10.53513/jsk.v6i1.7409
(9) Kade Bramasta Vikana Putra, I Putu Agung Bayupati, & Dewa Made Sri Arsa. (2021). Klasifikasi Citra Daging Menggunakan Deep Learning dengan Optimisasi Hard Voting. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(4), 656–662. https://doi.org/10.29207/resti.v5i4.3247
(10) Kapoor, A., Gulli, A., Pal, S., & Chollet, F. (2022). Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models. Packt Publishing. https://books.google.co.id/books?id=iq6REAAAQBAJ
(11) Laluma, R. H., Sugiarto, B., Santriyana, A., Azwar, A. G., Nurwathi, N., & Gunawan, G. (2021). Klasifikasi Perbedaan Daging Sapi Dan Daging Babi Dengan Metode Convolutional Neural Network Berbasis Web. Infotronik : Jurnal Teknologi Informasi dan Elektronika, 6(1), 1. https://doi.org/10.32897/infotronik.2021.6.1.603
(12) Lasniari, S., Jasril, J., Sanjaya, S., Yanto, F., & Affandes, M. (2022). Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra. Jurnal Sistem Komputer dan Informatika (JSON), 3(4), 450. https://doi.org/10.30865/json.v3i4.4167
(13) M., N., M., V., & Hemantha, G. (2020). Leaf Classification based on GLCM Texture and SVM. International Journal of Computer Applications, 177(35), 18–21. https://doi.org/10.5120/ijca2020919846
(14) Neneng, N., Puspaningrum, A. S., & Aldino, A. A. (2021). Perbandingan Hasil Klasifikasi Jenis Daging Menggunakan Ekstraksi Ciri Tekstur Gray Level Co-occurrence Matrices (GLCM) Dan Local Binary Pattern (LBP). Smatika Jurnal, 11(01), 48–52. https://doi.org/10.32664/smatika.v11i01.572
(15) Peryanto, A., Yudhana, A., & Umar, R. (2020). Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation. Journal of Applied Informatics and Computing, 4(1), 45–51. https://doi.org/10.30871/jaic.v4i1.2017
(16) Purnomo, T. Y., Yanto, F., Insani, F., Ramadhani, S., & Jasril. (2022). Penerapan Algoritma Random Forest Pada Klasifikasi Daging. Jurnal Intra Tech, 6(1).
(17) Sudibyo, U., Kusumaningrum, D. P., Rachmawanto, E. H., & Sari, C. A. (2018). Optimasi Algoritma Learning Vector Quantization (Lvq) Dalam Pengklasifikasian Citra Daging Sapi Dan Daging Babi Berbasis Glcm Dan Hsv. Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 9(1), 1–10. https://doi.org/10.24176/simet.v9i1.1943
(18) Winnarto, M. N., Mailasari, M., & Purnamawati, A. (2022). Klasifikasi Jenis Tumor Otak Menggunakan Arsitekture Mobilenet V2. Jurnal SIMETRIS, 13(2), 1–12.
(19) Wiryono, S. (2020). Fakta Kasus Daging Oplosan Sapi dan Babi di Tangerang, Kelabui Pembeli dengan Harga Murah. megapolitan.kompas.com. https://megapolitan.kompas.com/read/2020/05/19/07120811/fakta-kasus-daging-oplosan-sapi-dan-babi-di-tangerang-kelabui-pembeli?page=all
(20) Wu, J., Hao, X. C., Xiong, Z. L., & Lei, H. (2019). Optimasi Hiperparameter untuk Model Pembelajaran Mesin Berdasarkan Optimasi Bayesian. 17(1), 26–46.
(21) Yu, T., & Zhu, H. (2020). Hyper-Parameter Optimization: A Review of Algorithms and Applications. 1–56. http://arxiv.org/abs/2003.05689
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