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References
- I. Kartasujana and Suherdie, “4000 Jenis Pohon di Indonesia dan Index 4000 Jenis Kayu Indonesia (Berdasar Nama Daerah).†Badan Penelitian dan Pengembangan Kehutanan, 1993.
- T. Pulungan, “Terbesar di Dunia, Koleksi Kayu Perkuat Pangkalan Data Cadangan Karbon,†2018. [Online]. Available: https://nasional.sindonews.com/read/1360730/15/terbesar-di-dunia-koleksi-kayu-perkuat-pangkalan-data-cadangan-karbon-1544111835.
- E. Prakasa, H. F. Pardede, Y. Rianto, R. Damayanti, Krisdianto, and L. M. Dewi, “Development of Computer Vision Methods for Wood Identification,†no. September, 2017.
- A. . G. R. Gunawan, S. R. I. Nurdiati, and Y. Arkeman, “Identifikasi Jenis Kayu Menggunakan Support Vector Machine Berbasis Data Citra Wood Type Identification Using Support Vector Machine Based on Image Data,†J. Ilmu Komput. Agri Inform., vol. 3, pp. 1–8, 2014.
- I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning,†An MIT Press B., 2016.
- I. Wendianto Notonogoro, “Pengenalan Plat Nomor Indonesia menggunakan Convolutional Neural Network,†2018.
- Mathworks, “Introducing Deep Learning with MATLAB,†Introd. Deep Learn. with MATLAB, p. 15.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,†pp. 1097--1105, 2012.
- C. Szegedy et al., “Going deeper with convolutions,†arXiv1409.4842 [cs], pp. 1–9, 2014.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,†CoRR, vol. abs/1512.0, pp. 1–17, 2015.
- H.-C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.,†IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285–98, 2016.
- A. Veit, M. Wilber, and S. Belongie, “Residual Networks Behave Like Ensembles of Relatively Shallow Networks,†pp. 1–9, 2016.
- Y. Wu et al., “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation,†pp. 1–23, 2016.
- O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,†Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015.
References
I. Kartasujana and Suherdie, “4000 Jenis Pohon di Indonesia dan Index 4000 Jenis Kayu Indonesia (Berdasar Nama Daerah).†Badan Penelitian dan Pengembangan Kehutanan, 1993.
T. Pulungan, “Terbesar di Dunia, Koleksi Kayu Perkuat Pangkalan Data Cadangan Karbon,†2018. [Online]. Available: https://nasional.sindonews.com/read/1360730/15/terbesar-di-dunia-koleksi-kayu-perkuat-pangkalan-data-cadangan-karbon-1544111835.
E. Prakasa, H. F. Pardede, Y. Rianto, R. Damayanti, Krisdianto, and L. M. Dewi, “Development of Computer Vision Methods for Wood Identification,†no. September, 2017.
A. . G. R. Gunawan, S. R. I. Nurdiati, and Y. Arkeman, “Identifikasi Jenis Kayu Menggunakan Support Vector Machine Berbasis Data Citra Wood Type Identification Using Support Vector Machine Based on Image Data,†J. Ilmu Komput. Agri Inform., vol. 3, pp. 1–8, 2014.
I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning,†An MIT Press B., 2016.
I. Wendianto Notonogoro, “Pengenalan Plat Nomor Indonesia menggunakan Convolutional Neural Network,†2018.
Mathworks, “Introducing Deep Learning with MATLAB,†Introd. Deep Learn. with MATLAB, p. 15.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,†pp. 1097--1105, 2012.
C. Szegedy et al., “Going deeper with convolutions,†arXiv1409.4842 [cs], pp. 1–9, 2014.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,†CoRR, vol. abs/1512.0, pp. 1–17, 2015.
H.-C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.,†IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285–98, 2016.
A. Veit, M. Wilber, and S. Belongie, “Residual Networks Behave Like Ensembles of Relatively Shallow Networks,†pp. 1–9, 2016.
Y. Wu et al., “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation,†pp. 1–23, 2016.
O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,†Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015.