Model Prediksi Jenis Hewan dengan Metode Convolution Neural Network
Keywords:
CNN, Deep Learning, Prediksi Jenis BinatangAbstract
Proses komputasi pada komputer untuk melaksanakan suatu tugas tertentu tentunya tidak lepas dari metode pembelajaran. Dalam proses pembelajaran, berbagai metode dapat dilakukan untuk dapat memenuhi periode training tersebut untuk memberikan komputer suatu keahlian tertentu. Salah satu cara menunjang periode tersebut adalah dengan menggunakan algoritma deep learning convolution neural network (CNN). CNN mampu memuat keseluruhan skala informasi klasifikasi objek tanpa kehilangan keakuratannya. Tujuan dari penelitian ini adalah memberikan komputer kemampuan untuk mengenali jenis binatang dan memprediksi jenis binatang berdasarkan gambar yang dimasukan. Penelitian ini juga bertujuan untuk menilai keakuratan hasil training metode pembelajaran dibangkan dengan hasil keluaran dari pembelajaran. Metode yang digunakan dalam penelitian ini adalah mentraining secara komputasi, sejumlah gambar kucing dan anjing. Kemudian test akan dilakukan dengan cara yang sama setelah melalui tahapan konvulasi training. Hasil dari penelitian ini keakuratan hasil training mencapai 97,56%Downloads
References
D. Indarti, “KARAKTERISTIK KURVA SEDERHANA OBJECT RECOGNITION IN IMAGE BASED ON SIMILARITY,” vol. 20, no. 100.
D. Ricardo and G. Gasim, “Perbandingan Akurasi Pengenalan Jenis Beras dengan Algoritma Propagasi Balik pada Beberapa Resolusi Kamera,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 131–140, 2019, doi: 10.29207/resti.v3i2.894.
D. M. Hakim and E. Rainarli, “Convolutional Neural Network untuk Pengenalan Citra Notasi Musik,” Techno.Com, vol. 18, no. 3, pp. 214–226, 2019, doi: 10.33633/tc.v18i3.2387.
T. Trnovszky, P. Kamencay, R. Orjesek, M. Benco, and P. Sykora, “Animal recognition system based on convolutional neural network,” Adv. Electr. Electron. Eng., vol. 15, no. 3, pp. 517–525, 2017, doi: 10.15598/aeee.v15i3.2202.
C. Mora, D. P. Tittensor, S. Adl, A. G. B. Simpson, and B. Worm, “How many species are there on earth and in the ocean?,” PLoS Biol., vol. 9, no. 8, pp. 1–8, 2011, doi: 10.1371/journal.pbio.1001127.
Y. H. Zhang and X. Jia, “Republication of conference papers in journals?,” Learn. Publ., vol. 26, no. 3, pp. 189–196, 2013, doi: 10.1087/20130307.
J. L. Wu and W. Y. Ma, “A Deep Learning Framework for Coreference Resolution Based on Convolutional Neural Network,” Proc. - IEEE 11th Int. Conf. Semant. Comput. ICSC 2017, pp. 61–64, 2017, doi: 10.1109/ICSC.2017.57.
Z. Li, G. Chen, and T. Zhang, “Temporal attention networks for multitemporal multisensor crop classification,” IEEE Access, vol. 7, pp. 134677–134690, 2019, doi: 10.1109/ACCESS.2019.2939152.
Y. Lecun and Y. Bengio, “Convolutional Neural Networks for Images, Speech and Time-Series,” AT&T Bell Lab., 1995.
T. Purwaningsih, T. Nurhikmat, and P. B. Utami, “Image classification of Golek puppet images using convolutional neural networks algorithm,” Int. J. Adv. Soft Comput. its Appl., vol. 11, no. 1, pp. 34–45, 2019.
A. Shrestha and A. Mahmood, “Review of deep learning algorithms and architectures,” IEEE Access, vol. 7, pp. 53040–53065, 2019, doi: 10.1109/ACCESS.2019.2912200.
A. Chatterjee, J. Saha, J. Mukherjee, S. Aikat, and A. Misra, “Unsupervised Land Cover Classification of Hybrid and Dual-Polarized Images Using Deep Convolutional Neural Network,” IEEE Geosci. Remote Sens. Lett., pp. 1–5, 2020, doi: 10.1109/lgrs.2020.2993095.
K. Yin, Robert, Qualitative Research from Start to Finish 2nd Edition, 2nd ed. New York, 2016.
A. Agarwal, S. Gupta, and T. Choudhury, “Continuous and Integrated Software Development using DevOps,” 2018 Int. Conf. Adv. Comput. Commun. Eng., no. June, pp. 290–293, 2018, doi: 10.1109/icacce.2018.8458052.
S. Wolfert, L. Ge, C. Verdouw, and M. J. Bogaardt, “Big Data in Smart Farming – A review,” Agric. Syst., vol. 153, pp. 69–80, 2017, doi: 10.1016/j.agsy.2017.01.023.
W. Gunawan, “Pengembangan Aplikasi Berbasis Android Untuk Pengenalan Huruf Hijaiyah,” vol. 6, no. 1, pp. 69–76, 2019.
Downloads
Published
How to Cite
Issue
Section
License
The copyright to this article is transferred to Universitas Mercu Buana (UMB) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to UMB. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.
We declare that this paper has not been published in the same form elsewhere.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above-mentioned paper as a whole to UMB. The copyright transfer covers the right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
Our Articles are licensed under CC BY-NC
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
