Perbandingan Convolution Neural Network Untuk Klasifikasi Kesegaran Ikan Bandeng Pada Citra Mata

Penulis

Eko Prasetyo, Rani Purbaningtyas, Raden Dimas Adityo, Enrico Tegar Prabowo, Achmad Irfan Ferdiansyah

Abstrak

Ikan merupakan salah satu sumber protein hewani dan sangat diminati masyarakat Indonesia, dari survey bahan makanan yang diminati, bandeng peringkat keempat dibanding bahan makanan yang lain. Khususnya ikan bandeng, ikan ini menjadi satu dari enam ikan yang banyak dikonsumsi masyarakat selain tongkol, kembung, teri, mujair dan lele, maka ketelitian masyarakat ketika membeli ikan bandeng menjadi perhatian serius dalam memilih ikan bandeng segar. Deteksi kesegaran dengan menyentuh tubuh ikan dapat mengakibatkan kerusakan tanpa disengaja, maka deteksi kesegaran ikan harus dilakukan tanpa menyentuh ikan bandeng dengan memanfaatkan citra kondisi mata. Dalam riset ini, kami melakukan eksperimen implementasi klasifikasi kesegaran ikan bandeng sangat segar dan tidak segar berdasarkan mata menggunakan transfer learning dari empat CNN, yaitu Xception, MobileNet V1, Resnet50, dan VGG16. Dari hasil eksperimen klasifikasi dua kelas kesegaran ikan bandeng menggunakan 154 citra menunjukkan bahwa VGG16 mencapai kinerja terbaik dibanding arsitektur lainnya dimana akurasi klasifikasi mencapai 0.97. Dengan akurasi lebih tinggi dibanding arsitektur lainnya maka VGG16 relatif lebih tepat digunakan untuk klasifikasi dua kelas kesegaran ikan bandeng.

 

Abstract

Fish, one source of animal protein, is an exciting food for Indonesia's people. From a survey of food-ingredients demanded, milkfish are ranked fourth compared to other food-ingredients. Especially for milkfish, this fish is one of the six fish consumed by Indonesia's people besides tuna, bloating, anchovies, tilapia, and catfish, so the exactitude of the people when buying is a severe concern in choosing fresh milkfish. Detection of freshness by touching the fish's body may cause unexpected destruction, so detecting the fish's freshness should be conducted without touching using the eye image. In this research, we conducted an experimental implementation of freshness milkfish classification (vastly fresh and not fresh) based on the eyes using transfer learning from several CNNs, such as Xception, MobileNet V1, Resnet50, and VGG16. The experimental results of the classification of two milkfish freshness classes using 154 images show that VGG16 achieves the best performance compared to other architectures, where the classification accuracy achieves 0.97. With higher accuracy than other architectures, VGG16 is relatively more appropriate for classifying two classes of milkfish freshness.

Teks Lengkap:

PDF

Referensi


ABU MALLOUH, A., QAWAQNEH, Z. and BARKANA, B. D. (2019) ‘Utilizing CNNs and transfer learning of pre-trained models for age range classification from unconstrained face images’, Image and Vision Computing. Elsevier Ltd, 88, pp. 41–51. doi: 10.1016/j.imavis.2019.05.001.

Badan Pusat Statistik Indonesia (2015) Ringkasan Eksekutif Pengeluaran dan Konsumsi Penduduk Indonesia. Badan Pusat Statistik. Available at: https://media.neliti.com/media/publications/48424-ID-ringkasan-eksekutif-pengeluaran-dan-konsumsi-penduduk-indonesia-berdasarkan-hasi.pdf.

CHOLLET, F. (2017) ‘Xception: Deep learning with depthwise separable convolutions’, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. doi: 10.1109/CVPR.2017.195.

DARMANTO, H. (2019) ‘Pengenalan Spesies Ikan Berdasarkan Kontur Otolith Menggunakan Convolutional Neural Network’, Joined Journal (Journal of Informatics Education), 2(1), pp. 41–59. doi: 10.31331/joined.v2i1.847.

DEEPAK, S. and AMEER, P. M. (2019) ‘Brain tumor classification using deep CNN features via transfer learning’, Computers in Biology and Medicine. Elsevier Ltd, 111, p. 103345. doi: 10.1016/j.compbiomed.2019.103345.

DWIYATNO, S., IKSAL, I. and NUGRAHA, S. (2018) ‘Alat Pendeteksi Kesegaran Ikan Menggunakan Metode K-nearest Neighbor Berdasar Warna Mata Berbasis ATMEGA 328’, PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer, 5(2). Available at: https://e-jurnal.lppmunsera.org/index.php/PROSISKO/article/view/789.

HE, K. et al. (2016) ‘Deep residual learning for image recognition’, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. doi: 10.1109/CVPR.2016.90.

HOWARD, A. G. et al. (2017) ‘MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications’. Available at: http://arxiv.org/abs/1704.04861 (Accessed: 25 November 2019).

HUANG, G. et al. (2017) ‘Densely connected convolutional networks’, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. doi: 10.1109/CVPR.2017.243.

ILYAS (1983) Teknologi Refrigerasi Hasil Perikanan Jilid 1 Teknik Pendingin Ikan. Edited by B. P. dan P. Pertanian. Jakarta: Paripurna.

Kelautan dan Perikanan, K. (2016) ‘Peta Sentra Produksi Perikanan Budidaya’. Edited by D. P. dan U. B. Direktorat Jenderal Perikanan Budidaya. Jakarta.

KRIZHEVSKY, A., SUTSKEVER, I. and HINTON, G. E. (2012) ‘ImageNet classification with deep convolutional neural networks’, in Advances in Neural Information Processing Systems.

PRASETYO, E., ADITYO, R. DIMAS and PURBANINGTYAS, R. (2019) ‘Classification of segmented milkfish eyes using cosine K-nearest neighbor’, in Proceedings of ICAITI 2019 - 2nd International Conference on Applied Information Technology and Innovation: Exploring the Future Technology of Applied Information Technology and Innovation. Institute of Electrical and Electronics Engineers Inc., pp. 93–98. doi: 10.1109/ICAITI48442.2019.8982124.

PRASETYO, E., ADITYO, RADEN DIMAS and PURBANINGTYAS, R. (2019) ‘Segmentasi Mata Ikan Bandeng dengan Klasifikasi’, in CITEE, pp. 213–219.

PRASETYO, E., PURBANINGTYAS, R. and DIMAS ADITYO, R. (2020) ‘Cosine K-Nearest Neighbor in Milkfish Eye Classification’, International Journal of Intelligent Engineering and Systems, 13(3). doi: 10.22266/ijies2020.0630.02.

PRAYOGI, Y. R., WIBISONO, C. L. and ABROR, A. H. (2019) ‘Deteksi Kesegaran Ikan Bandeng Berbasis Pengolahan Citra Digital’, REMIK (Riset dan E-Jurnal Manajemen Informatika Komputer). Politeknik Ganesha, 4(1), p. 53. doi: 10.33395/remik.v4i1.10228.

SAPUTRA, R., MATULATAN, T. and HAYATY, N. (2020) Pengelompokan Kesegaran Ikan Melalui Citra Mata Ikan Menggunakan Metode CNN (Convolution Neural Network), Student Online Journal (SOJ) UMRAH - Teknik.

SHARMA, P., BERWAL, Y. P. S. and GHAI, W. (2019) ‘Performance analysis of deep learning CNN models for disease detection in plants using image segmentation’, Information Processing in Agriculture. China Agricultural University. doi: 10.1016/j.inpa.2019.11.001.

SIMONYAN, K. and ZISSERMAN, A. (2015) ‘Very deep convolutional networks for large-scale image recognition’, in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings.

SZEGEDY, C. et al. (2016) ‘Rethinking the Inception Architecture for Computer Vision’, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. doi: 10.1109/CVPR.2016.308.

TAN, M. and LE, Q. V. (2019) ‘EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks’, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), 2019-June, pp. 10691–10700. Available at: http://arxiv.org/abs/1905.11946 (Accessed: 11 June 2020).

VASAN, D. et al. (2020) ‘IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture’, Computer Networks. Elsevier B.V., 171, p. 107138. doi: 10.1016/j.comnet.2020.107138.




DOI: http://dx.doi.org/10.25126/jtiik.2021834369