Implementasi Algoritma Convolutional Neural Network dan YOLOV8 Untuk Klasifikasi Ras Kucing
Abstract
The cat with the scientific name Felis catus is a very popular pet and is often kept in various parts of the world. There are many types or breeds of cats, each of which has its own characteristics and characteristics, such as style, body shape, fur and color. However, because of the many breeds and the uniqueness of each breed, it is often difficult for ordinary people to differentiate between the types of cat breeds that exist. Therefore, technology is needed to identify and differentiate cat breeds. By comparing the Convolutional Neural Network (CNN) and YOLOV8 methods, this research aims to develop a cat breed classification model. This research uses data from six different cat breeds, namely Bengal, Bombay, Himalayan, Local, Persian and Sphynx. There are 1,200 images in all, with 200 images for each race. Before the data is used for training with the CNN and YOLOV8 methods, a preprocessing stage is carried out which includes resize and rescale for the CNN method, while for YOLOV8 a data labeling process is carried out. There are two parts to the dataset: 20% validation data and 80% training data. The training process is carried out with the same parameters for each model, namely a learning rate of 0.001, batch size of 15, and 100 epochs. From the test results with the confusion matrix, the YOLOV8 model shows the best performance with an accuracy value of 99%, precision 96.1%, recall 98.4%, and F1-score 97.2%.
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M. Afif, A. Fawwaz, K. N. Ramadhani, and F. Sthevanie, “Klasifikasi Ras pada Kucing menggunakan Algoritma Convolutional Neural Network ( CNN ),” Jurnal Tugas Akhir Fakultas Informatika, vol. 8, no. 1, pp. 715–730, 2021.
A. Esteva et al., “Deep learning-enabled medical computer vision,” npj Digit. Med., vol. 4, no. 1, pp. 1–9, 2021, doi: 10.1038/s41746-020-00376-2.
P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network ( Cnn ) Pada Ekspresi Manusia,” Algor, vol. 2, no. 1, pp. 12–21, 2020.
A. Antoni, T. Rohana, and A. R. Pratama, “Implementasi Algoritma Convolutional Neural Network Untuk Klasifikasi Citra Kemasan Kardus Defect dan No Defect,” Build. Informatics, Technol. Sci., vol. 4, no. 4, pp. 1941–1950, 2023, doi: 10.47065/bits.v4i4.3270.
A. Harun, Mustakim, and O. B. Kharisma, “Implementasi Deep Learning Menggunakan Metode You Only Look Once untuk Mendeteksi Rokok,” J. Media Inform. Budidarma, vol. 7, no. 1, pp. 107–116, 2023, doi: 10.30865/mib.v7i1.5409.
J. Du, “Understanding of Object Detection Based on CNN Family and YOLO,” J. Phys. Conf. Ser., vol. 1004, no. 1, 2018, doi: 10.1088/1742-6596/1004/1/012029.
T. Al et al., “PEOPLE COUNTING FOR PUBLIC TRANSPORTATIONS USING YOU ONLY LOOK ONCE METHOD,” Jurnal Teknik Informatika (JUTIF), vol. 2, no. 1, pp. 57–66, 2021, doi: 10.20884/1.jutif.2021.2.2.77.
J. Terven and D. Cordova-Esparza, “A Comprehensive Review of YOLO: From YOLOv1 and Beyond,” arXiv (Cornell University), pp. 1–34, 2023, doi: 10.48550/arXiv.2304.00501.
K. Ahmad Baihaqi and C. Zonyfar, “Deteksi Lahan Pertanian Yang Terdampak Hama Tikus Menggunakan Yolo v5,” Syntax J. Inform., vol. 11, no. 02, pp. 01–11, 2022, doi: 10.35706/syji.v11i02.7226.
N. Azahro Choirunisa, T. Karlita, and R. Asmara, “Deteksi Ras Kucing Menggunakan Compound Model Scaling Convolutional Neural Network,” Technomedia J., vol. 6, no. 2, pp. 236–251, 2021, doi: 10.33050/tmj.v6i2.1704.
M. Ismu Rahayu, “KLASIFIKASI RAS KUCING MENGGUNAKAN METADATA DATASET KAGGLE DENGAN FRAMEWORK YOLO v5,” J. Teknol. Inf. dan Komun., vol. 12, no. 1, pp. 14–18, 2023, doi: 10.58761/jurtikstmikbandung.v12i1.1418.
J. Kusuma, A. Jinan, M. Z. Lubis, and R. Rosnelly, “Komparasi Algoritma Support Vector Machine Dan Naive Bayes Pada Klasifikasi Ras Kucing,” J. Generic, vol. 14, no. 1, pp. 8–12, 2022, doi: 10.18495/generic.v14i1.122.
A. A. B, A. Amin, and M. W. Kasrani, “Penerapan Metode Yolo Object Detection V1 Terhadap Proses Pendeteksian Jenis Kendaraan Di Parkiran,” J. Tek. Elektro Uniba (JTE UNIBA), vol. 6, no. 1, pp. 194–199, 2021, doi: 10.36277/jteuniba.v6i1.130.
K. A. Baihaqi and Y. Cahyana, “Application of Convolution Neural Network Algorithm for Rice Type Detection Using Yolo v3,” Systematics, vol. 3, no. 2, pp. 272–280, 2021, doi: 10.35706/sys.v3i2.5874.
Y. Pratama and E. Rasywir, “Eksperimen Penerapan Sistem Traffic Counting dengan Algoritma YOLO (You Only Look Once) V.4,” J. Media Inform. Budidarma, vol. 5, no. 4, pp. 1438–1446, 2021, doi: 10.30865/mib.v5i4.3309.
D. Reis, J. Kupec, J. Hong, and A. Daoudi, “Real-Time Flying Object Detection with YOLOv8,” arXiv (Cornell University), pp. 1-10, 2023, doi: 10.48550/arXiv.2305.09972.
M. Jamal, S. Faisal, D. S. Kusumaningrum, and T. Rohana, “APPLICATION OF YOLO V8 FOR PRODUCT DEFECT DETECTION IN MANUFACTURING COMPANIES,” Jusikom Prima, vol. 8, no. 1, pp. 1–11, 2024.
F. F. Maulana and N. Rochmawati, “Klasifikasi Citra Buah Menggunakan Convolutional Neural Network,” J. Informatics Comput. Sci., vol. 1, no. 02, pp. 104–108, 2020, doi: 10.26740/jinacs.v1n02.p104-108.
S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018, doi: 10.32528/justindo.v3i2.2254.
A. Santoso and G. Ariyanto, “Implementasi Deep Learning berbasis Keras untuk Pengenalan Wajah,” Emit. J. Tek. Elektro, vol. 18, no. 1, pp. 15–21, 2018, doi: 10.23917/emitor.v18i01.6235.
K. Ahmad Baihaqi, C. Zonyfar, and B. Nugraha, “PENGENALAN JENIS CANDI BERDASARKAN BENTUK DAN MODELNYA MENGGUNAKAN MOTODE CONVOLUTIONAL NEURAL NETWORK (CNN) PADA YOLLO v3,” Syntax J. Inform., vol. 10, no. 02, pp. 13–23, 2021, doi: 10.35706/syji.v10i02.5665.
M. D. Anggraini and H. Al Fatta, “SOCIAL DISTANCING DETECTION FINDING OPTIMAL ANGLE WITH YOLO V3 DEEP LEARNING METHOD,” Jurnal Teknik Informatika (JUTIF), vol. 3, no. 5, pp. 1449–1454, 2022, doi: 10.20884/1.jutif.2022.3.5.390.
B. Wang, “Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8,” arXiv (Cornell University), pp. 5349–5357, 2023, doi: 10.48550/arXiv.2304.08256.
E. Oktafanda, “Klasifikasi Citra Kualitas Bibit dalam Meningkatkan Produksi Kelapa Sawit Menggunakan Metode Convolutional Neural Network (CNN),” J. Inform. Ekon. Bisnis, vol. 4, no. 3, pp. 72–77, 2022, doi: 10.37034/infeb.v4i3.143.
M. A. Rohman and D. Arifianto, “Penerapan Metode Euclidean Probality dan Confusion Matrix dalam Diagnosa Penyakit Koi,” J. Smart Teknol., vol. 2, no. 2, pp. 122–130, 2021.
V. Sajwan and R. Ranjan, “Classifying flowers images by using different classifiers in orange,” Int. J. Eng. Adv. Technol., vol. 8, no. 6 Special Issue 3, pp. 1057–1061, 2019, doi: 10.35940/ijeat.F1334.0986S319.
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