Classification of cendrawasih birds using convolutional neural network (CNN) keras recognition


Warnia Nengsih(1*); Ardiyanto Ardiyanto(2); Ayu Putri Lestari(3);

(1) Politeknik Caltex Riau
(2) Politeknik Caltex Riau
(3) Politeknik Caltex Riau
(*) Corresponding Author

  

Abstract


Classification is part of predictive modeling and supervised learning. This method is used to determine the data class based on the previous value. In solving certain cases, there are various classification methods with varying degrees of accuracy. Convolutional Neural Network (CNN) is part of the Multilayer Perceptron (MLP) for processing two-dimensional data. CNN is also part of the Deep Neural Network and is applied to image objects. From several sources, it is stated that the classification process using images is not properly implemented in this MLP. Of course, this will result in the accuracy of the method in handling certain cases. In this study, the object classification process uses hard recognition to determine the accuracy value of the method using the object of the bird of paradise. From the results of this study, a training model was conducted using 10 ephocs with an accuracy value of 0.0850 while a loss value of 2.5658. So these results indicate that MLP can successfully complete the classification process using images.

Keywords


Tensor; CNN Keras; Classification

  
  

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doi  https://doi.org/10.33096/ilkom.v13i3.865.259-265 		  

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References


Bramer, M., Principles of Data Mining. London: Springer. 2007.

Han, J., Kamber, M., & Pei, J., Data Mining Concepts and Techniques Third Edition. Waltham: Elsevier Inc. 2011.

Joseph, R., S. Divvala, R. Girshick and F.-h. Ali.,. You only look once: Unified, Real-Time Object Detection, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, 779-788.

Krizhevsky, A., A. Sutskever and G. E. Hikton., ImageNet Classification with Deep Convolutional Neural Networks. Adv. Neural Info. Proc. Syst. 25: 2012, 1097-1105.

Lazaro, A., Bulial, J. L., & Amaliah, B., Deteksi Jenis Kendaraan di Jalan Menggunakan OpenCV. 6, 2017.

Meilina, P., Penerapan Data Mining Dengan Metode Kalsifikasi Menggunakan Decision Tree dan Regresi, 2015.

BKN. Statistik PNS per Desember 2018. Retrieved Juli Kamis, 2019 from Badan Kepegawaian Negara: http://www.bkn.go.id

Saleh, A., Klasifikasi Gejala Depresi Pada Manusia dengan MetodeNave Bayes Menggunakan Java. Yogyakarta, 2015.

Suartika, I. Y., Wijaya, A. Y., & Soelaiman, R., Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101. Jurnal Teknik ITS Vol. 5, No. 1, 2016.

Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich., Going Deeper with Convolutions, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 1-9.

A.Coates, H.Lee, and A.Y. Ng. (2011). An Analysis of Single-Layer Network in Unsupervised Feature Learning.

Alpaydin, E. (2009). Introduction to Machine Learning, Second Edition. London: MIT Press Budianita.

E., Jasril. (2015). Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbor untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi. Jurnal Sains, Teknologi dan Industri, 242- 247.

Goodfellow, I., Bengio, Y, and Courville, A. (2016). Deep Learning (Adaptive Computation and Mechine Learning Series). The IMT Press.

Hubel, D., and Wiesel, T. (1968). Receptive Fields and Functional architecture of monkey striate kortex. Journal of Physiology (London), 195, 215-243 92


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