Identifikasi Jenis Anjing Berdasarkan Gambar Menggunakan Convolutional Neural Network Berbasis Android
Keywords:
Realistic fiction, serial rapist, criminalAbstract
Dogs are raised by many people however, to maintain a dog, there are several factors that must be considered such as feed consumed, intensity of care, and cleanliness of the cage or the appropriate environment. Therefore, an android application is needed to identify the type of dog and provide information related to the type of dog. The method we used is You Only Look Once to detect dog objects in an image then the dog image is cropped, the results will be processed by the Convolutional Neural Network to identify the type of dog based on the image given after that displaying the results of its identification on android. The test results show that the identification results from CNN are very dependent on the results of predictions from YOLO because the input from CNN is the result of predictions from YOLO. YOLO has a disadvantage where it will detect dog dolls and dog fur as dog objects. The test results show the accuracy of YOLO to detect dogs is 94.242%, CNN accuracy of model I is 56.400%, accuracy of CNN model II is 40,000% and accuracy of CNN model III is 50.400%.References
[1] Abhirawa, H. 2017. Pengenalan Wajah Menggunakan
Convolutional Neural Network. e-Proceeding of Engineering,
4907-4916.
URI= http://repositori.usu.ac.id/handle/123456789/15450
[2] Arrofiqoh, E. N., & Harintaka. 2018. Implementasi Metode
Convolutional Neural Network untuk Klasifikasi Tanaman
pada Citra Resolusi Tinggi. Geomatika , 61-68.
URI= http://garuda.ristekbrin.go.id/documents/detail/843166
[3] Dewa, C. K. 2018. Convolutional Neural Networksfor
Handwritten Javanese Character Recognition. IJCCS
(Indonesian Journal of Computing and Cybernetics Systems),
83-94.
DOI= https://doi.org/10.22146/ijccs.31144
[4] Du, J. 2018. Understanding of Object Detection Based on
CNN Family and YOLO. Journal of Physics: Conference
Series, 1-8.
URI= https://iopscience.iop.org/article/10.1088/1742-
6596/1004/1/012029
[5] Jupiyandi, S., Saniputra, F. R., Pratama, Y., & Dharmawan,
M. R. 2019. Pengembangan Deteksi Citra Mobil Untuk
Mengetahui Jumlah Tempat Parkir Menggunakan Cuda dan
Modified YOLO. Jurnal Teknologi Informasi dan Ilmu
Komputer (JTIIK), 413-419.
URI=http://jtiik.ub.ac.id/index.php/jtiik/article/view/1275/pd
f
[6] Ouyang, J., He, H., He, Y., & Tang, H. 2019. Dog recognition
in public places based on convolutional neural network.
International Journal of Distributed Sensor Networks, 1-9.
URI= https://journals.sagepub.com/doi/10.1177/1550147719
829675?icid=int.sj-full-text.similar-articles.2
[7] Pangestu, M. A., & Bunyamin, H. 2018. Analisis Performa
dan Pengembangan Sistem Deteksi Ras Anjing pada Gambar
dengan Menggunakan Pre-Trained CNN Model. Jurnal
Teknik Informatika dan Sistem Informasi , 337-344.
URI= https://journal.maranatha.edu/index.php/jutisi/article/
view/1501
[8] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. 2015.
You Only Look Once: Unified, Real-Time Object Detection.
URI= https://arxiv.org/abs/1506.02640
[9] Rivan, M. E., & Yohannes. 2019. Klasifikasi Mamalia
Berdasarkan Bentuk Wajah Dengan K-NN Menggunakan
Fitur CAS Dan HOG. Jurnal Teknik Informatika dan Sistem
Informasi , 173-180.
URI=https://doi.org/10.35957/jatisi.v5i2.139
[10] Salim, S. D., & Suryadibrata, A. 2019. Klasifikasi Anjing dan
Kucing menggunakan Algoritma Linear Discriminant
Analysis dan Support Vector Machine. ULTIMATICS, 46-51.
URI= https://doi.org/10.31937/ti.v11i1.1076
[11] Shianto, K. A. 2019. Deteksi Jenis Mobil Menggunakan
Metode YOLO Dan Faster R-CNN. Jurnal Infra Vol 7, No 1,
157-163.
URI= http://publication.petra.ac.id/index.php/teknikinformatika/article/view/8065
[12] Ulfa, Z., Elfidasari, D., & Sugoro, I. 2016. Identifikasi Khamir
Patogen pada Kulit dan Telinga Anjing Peliharaan. ALAZHAR INDONESIA SERI SAINS DAN TEKNOLOGI, 213-
220.
URI= https://jurnal.uai.ac.id/index.php/SST/article/view/236
[13] Wirawan, L. V. 2002. Sistem pengenalan plat nomor
kendaraan bermotor dengan metode principal components
analysis.
URI=http://jurnalelektro.petra.ac.id/index.php/elk/article/
view