Klasifikasi Jenis Ayam Menggunakan Ensemble Voting CNN (Mobilenetv2 Dan Efficientnet-B0) Berbasis Citra

Sudanto; Joni Karman; Satrianansyah; Budi Santoso

doi:10.47065/bit.v7i3.3067

Sudanto * Universitas Bina Insan, Indonesia
Joni Karman Universitas Bina Insan, Indonesia
Satrianansyah Universitas Bina Insan, Indonesia
Budi Santoso Universitas Bina Insan, Indonesia

DOI: https://doi.org/10.47065/bit.v7i3.3067

Keywords: Ensemble Soft Voting; MobileNetV2; EfficientNet-B0; Chicken Breed Classification; Convolutional Neural Network (CNN)

Abstract

Digital image-based chicken species classification plays a crucial role in supporting the identification process in the livestock sector. However, the similarity in visual characteristics between chicken species makes the manual classification process inefficient and error-prone. This study aims to develop a chicken species classification model using the Ensemble Soft Voting method by combining two Convolutional Neural Network (CNN) architectures, namely MobileNetV2 and EfficientNet-B0. The dataset used consists of 1,500 images from five chicken classes: Bangkok Chicken, Broiler Chicken, Free-Range Chicken, Bantam Chicken, and Laying Hen. The research stages include preprocessing, data augmentation, transfer learning, model training, and combining prediction probabilities using the Soft Voting method. Evaluation is carried out using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix metrics. The results showed that MobileNetV2 achieved an accuracy of 97.18%, EfficientNet-B0 achieved 96.48%, while the Ensemble Soft Voting method produced the best performance with an accuracy of 97.89%, a precision of 97.96%, a recall of 97.89%, and an F1-score of 97.88%. These results indicate that the Ensemble Soft Voting method is able to improve classification accuracy and stability compared to using a single model, so it has the potential to be applied as a solution for digital image-based chicken species identification.

References

S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah, “Transformers in Vision: A Survey,” ACM Comput. Surv., vol. 54, no. 10, pp. 1–30, 2022, doi: 10.1145/3505244.

M. Tan and Q. V. Le, “EfficientNetV2: Smaller Models and Faster Training,” Proc. Mach. Learn. Res., vol. 139, pp. 10096–10106, 2021.

M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 10691–10700, 2019.

A. F. Husen, O. Sjofjan, Y. A. Tribudi, A. P. Z. N. L. Sari, and V. M. Ani Nurgiartiningsih, “Phenotypic characterization and principal component analyses of Bangkok chicken in Indonesia,” J. Appl. Anim. Res., vol. 52, no. 1, 2024, doi: 10.1080/09712119.2024.2417047.

A. A. A. Abdel-Wareth, A. A. Ahmed, M. O. Taqi, M. Salahuddin, and J. Lohakare, Artificial Intelligence Applications in Animal Production Systems for Climate Resilience and Sustainability: A Comprehensive Review, vol. 16, no. 11. 2026. doi: 10.3390/agriculture16111146.

N. Wangsa Kencana and R. Umar, “Implementasi Transfer Learning Untuk Klasifikasi Jenis Ras Ayam Menggunakan Arsitektur MobileNetV2,” vol. 7, no. 6, pp. 147–154, 2024.

A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2017, [Online]. Available: http://arxiv.org/abs/1704.04861

Y. I. Sulistya, E. T. Br Bangun, and D. A. Tyas, “CNN Ensemble Learning Method for Transfer learning: A Review,” Ilk. J. Ilm., vol. 15, no. 1, pp. 45–63, 2023, doi: 10.33096/ilkom.v15i1.1541.45-63.

B. Antonio, D. Moroni, and M. Martinelli, “Efficient adaptive ensembling for image classification,” Expert Syst., vol. 42, no. 1, pp. 1–25, 2025, doi: 10.1111/exsy.13424.

A. Al Shafi, A. Muntakim, P. C. Shill, R. Zannat, and A. Al-Amin, “Skin Lesion Classification Using a Soft Voting Ensemble of Convolutional Neural Networks,” 2025 Int. Conf. Electr. Comput. Commun. Eng. ECCE 2025, no. February, pp. 13–15, 2025, doi: 10.1109/ECCE64574.2025.11013422.

X. S. Wei et al., “Fine-Grained Image Analysis With Deep Learning: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 12, pp. 8927–8948, 2022, doi: 10.1109/TPAMI.2021.3126648.

D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–15, 2015.

Johan Wijaya Kusuma; Julian Supardi, “SOFT VOTING ENSEMBLE AND FINE-TUNED APPROACH TO IMAGE,” pp. 277–286, 2026.

D. N. Sari, Y. Rahsel, A. Jayadi, P. Andhika, and D. E. Kurniawan, “Implementation of MobileNetV2 Transfer Learning for Chicken Egg Quality Classification Using Jetson Nano,” pp. 77–93, 2026.

R. V. Nahari, Anisyafaah, and Riza Alfita, “Poultry Disease Classification Using EfficientNetV2-L and MobileNetV2 Based on Fecal Images,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 11, no. 3 SE-, Jun. 2026, doi: 10.22219/kinetik.v11i3.2648.

C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, 2019, doi: 10.1186/s40537-019-0197-0.

M. Sandler, M. Zhu, A. Zhmoginov, and C. V Mar, “MobileNetV2: Inverted Residuals and Linear Bottlenecks”.

A. Sa’idu, N. B. Kadandani, and B. B. Bukata, “A review of convolutional neural networks for classifying power quality problems using Keras API,” Indones. J. Electr. Eng. Comput. Sci., vol. 38, no. 1, p. 1, 2025, doi: 10.11591/ijeecs.v38.i1.pp1-21.

L. I. Kuncheva, COMBINING PATTERN CLASSIFIERS. 2014. doi: 10.1002/9781118914564.ch9.

D. M. W. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” pp. 37–63, 2020, [Online]. Available: http://arxiv.org/abs/2010.16061