Klasifikasi Hewan Anjing, Kucing, dan Harimau Menggunakan Metode Convolutional Neural Network (CNN)
DOI:
https://doi.org/10.14421/jiska.2025.10.3.331-340Keywords:
Klasifikasi hewan, Convolutional Neural Network (CNN), Deep learning, Pengolahan citra, Identifikasi spesiesAbstract
Animal classification is a complex challenge due to variations in shape, color, and patterns across species. Traditional methods, which rely on manual feature extraction, are often ineffective in handling such complexities. Therefore, this study employs Convolutional Neural Networks (CNNs) as a more accurate approach for automatic feature extraction and image classification. This research aims to develop an animal image classification model, specifically for dogs, cats, and tigers, utilizing CNNs. The dataset consists of 4,800 images obtained from Kaggle, which were divided into training, testing, and validation sets. The CNN model was built using TensorFlow/Keras, trained for 50 epochs, and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The experimental results show that the model achieved an overall accuracy of 88%, with the highest performance in tiger classification (99% accuracy). However, distinguishing between dogs and cats remains a challenge, with an accuracy of 81% for both classes. The findings indicate that CNNs are effective in automatically classifying animal images, although challenges persist in differentiating visually similar species. This study lays the groundwork for further enhancements, such as refining the model architecture or utilizing data augmentation techniques to boost classification accuracy.
References
Adrianto, H., Setyawan, Y., Banjarnahor, D. P., Kusumah, I. P., & Messakh, B. D. (2022). Pembekalan Klasifikasi Baru Makhluk Hidup Hewan Kepada Guru-Guru Biologi. Sebatik, 26(2), 638–643. https://doi.org/10.46984/sebatik.v26i2.2152
Ahmad, A., Idris, I. S. K., & Bode, A. (2023). Klasifikasi Jenis Buah Tomat Menggunakan Convolutional Neural Network. Jurnal Ilmiah Ilmu Komputer, 2(2), 83–89. https://doi.org/10.37195/balok.v2i2.617
A’yun, D. K., & Erman, E. (2019). Kemampuan Siswa Mengklarifikasi Kingdom Animalia Invertebrata: Studi Kasus di SMP Negeri 1 Jabon. PENSA: E-Jurnal Pendidikan Sains, 7(3), 361–366. https://ejournal.unesa.ac.id/index.php/pensa/article/view/32294
Fukushima, K. (1980). Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position. Biological Cybernetics, 36(4), 193–202. https://doi.org/10.1007/BF00344251
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2020). Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 386–397. https://doi.org/10.1109/TPAMI.2018.2844175
Hidayatullah, D., Ardiansah, T., & Styawati, S. (2022). Sistem Informasi Reservasi Pelayanan dan Penyewaan Fasilitas Lapangan Futsal Berbasis Web dengan Metode Waterfall. Jurnal Teknologi dan Sistem Informasi (JTSI), 3(3), 64–68. https://doi.org/10.33365/jtsi.v3i3.1994
K, L., Gadde, S., Puttagunta, M. K., Dhanalakshmi, G., & El-Ebiary, Y. A. B. (2023). Efficiency Analysis of Firefly Optimization-Enhanced GAN-Driven Convolutional Model for Cost-Effective Melanoma Classification. International Journal of Advanced Computer Science and Applications, 14(11), 742–753. https://doi.org/10.14569/IJACSA.2023.0141175
Laksono, S. A., Rahmat, B., & Nugroho, B. (2024). Identifikasi Jenis Ikan Cupang Berdasarkan Gambar Menggunakan Metode Convolutional Neural Network. JATI (Jurnal Mahasiswa Teknik Informatika), 8(3), 3331–3338. https://doi.org/10.36040/jati.v8i3.9676
Leovincent, A., & Yoannita, Y. (2023). Klasifikasi Ras Anjing Berdasarkan Citra Menggunakan Convolutional Neural Network. Jurnal Algoritme, 3(2), 160–169. https://doi.org/10.35957/algoritme.v3i2.3389
Mariyanti, S., Gayatri, Y., & Wikanta, W. (2022). Pengembangan Atlas Klasifikasi Hewan Vertebrata Berbasis Sumber Daya Hayati Lokal Sebagai Sumber Belajar Biologi di Sekolah Penulis. Journal of Science, Education, and Studies, 1(1), 1–9. https://journal.um-surabaya.ac.id/index.php/J-SES/article/view/14877
Micheal, M., & Hartati, E. (2022). Klasifikasi Spesies Kupu Kupu Menggunakan Metode Convolutional Neural Network. MDP Student Conference (MSC) 2022, 569–577. https://jurnal.mdp.ac.id/index.php/msc/article/view/1928
Mohite, O. S., Jørgensen, T. S., Booth, T. J., Charusanti, P., Phaneuf, P. V., Weber, T., & Palsson, B. O. (2025). Pangenome Mining of the Streptomyces Genus Redefines Species’ Biosynthetic Potential. Genome Biology, 26(1), Article ID: 9. https://doi.org/10.1186/s13059-024-03471-9
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018). Automatically Identifying, Counting, and Describing Wild Animals in Camera-Trap Images with Deep Learning. Proceedings of the National Academy of Sciences, 115(25), E5716–E5725. https://doi.org/10.1073/pnas.1719367115
Openg, J. B. J. R., Hiswati, M. E., & Hamzah, H. (2022). Klasifikasi Unggas Ordo Anseriformes Berdasarkan Citra Menggunakan Metode Deep Learning dengan Algoritma Convolutional Neural Network (CNN). 7th Seminar Nasional Teknik Elektro, Informatika dan Sistem Informasi (SINTaKS), 1. https://doi.org/10.35842/sintaks.v1i1.3
Ricard, A., Crevier-Denoix, N., Pourcelot, P., Crichan, H., Sabbagh, M., Dumont-Saint-Priest, B., & Danvy, S. (2023). Genetic Analysis of Geometric Morphometric 3D Visuals of French Jumping Horses. Genetics Selection Evolution, 55(1), Article ID: 63. https://doi.org/10.1186/s12711-023-00837-8
Samudra, J. T., Rosnelly, R., Situmorang, Z., & Ramadhan, P. S. (2023). Model Klasifikasi Jenis Hewan dengan SVM, KNN, Logistic Regression Menggunakan Pre-Trained VGG 16. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer), 22(2), 225–231. https://doi.org/10.53513/jis.v22i2.8314
Suhendar, S., Purnama, A., & Fauzi, E. (2023). Deteksi Penyakit pada Daun Tanaman Ubi Jalar Menggunakan Metode Convolutional Neural Network. Jurnal Ilmiah Informatika Global, 14(3), 62–67. https://doi.org/10.36982/jiig.v14i3.3478
Supiyandi, S., Sitorus, A., Fitriah, N., Virul, H., & Rangkuti, S. P. (2024). Pendeteksi Gerakan pada Vidio Menggunakan Pyton dan OpenCV. Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika, 2(6), 334–343. https://doi.org/10.61132/merkurius.v2i6.522
Tan, M., & Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In K. Chaudhuri & R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 6105–6114). PMLR. https://proceedings.mlr.press/v97/tan19a.html
Younis, E. M. G., Zaki, S. M., Kanjo, E., & Houssein, E. H. (2022). Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion. Sensors, 22(15), Article ID: 5611. https://doi.org/10.3390/s22155611
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