Implementasi Algoritma Convolutional Neural Network Untuk Identifikasi Jenis Kelamin Dan Ras: Implementation Of Convolutional Neural Network Algorithm For Gender And Race Identification

Notasonda Soni  Putra; Benedika Ferdian  Hutabarat; Ulfa Khaira

doi:10.51454/decode.v3i1.123

Authors

Notasonda Soni Putra Sistem Informasi Universitas Jambi
Benedika Ferdian Hutabarat Sistem Informasi Universitas Jambi
Ulfa Khaira Sistem Informasi Universitas Jambi

DOI:

https://doi.org/10.51454/decode.v3i1.123

Keywords:

Convolutional Neural Network, Jenis Kelamin, Ras

Abstract

Citra pada wajah manusia memiliki banyak informasi yang bisa didapatkan, diantaranya yaitu informasi mengenai jenis kelamin, usia, ras, dan juga ekspresi. Untuk mendapatkan informasi tersebut maka diperlukan proses identifikasi citra pada wajah manusia dengan menggunakan teknologi deep learning. Salah satu algoritma yang terdapat dalam teknologi deep learning adalah algoritma Convolutional Neural Network. Pada penelitian ini dataset yang digunakan terdiri dari UTKFace dataset, CelebA dataset, Racial Faces in-the-Wild (RFW) dataset, Fairface dataset, dan Chicago Face (CFD) dataset. Pengujian dilakukan dengan jumlah data citra pada data jenis kelamin sebanyak 36.000 citra dan 27.000 citra untuk data ras dengan menggunakan dua skenario, yaitu dengan menggunakan batch_size sebesar 15 dan 30, serta dengan menggunakan jumlah epoch sebanyak 10 dan 50. Dari hasil pengujian didapatkan nilai akurasi rata-rata tertinggi untuk ras berada di batch 30 dan epoch 50 dengan nilai akurasi rata-rata sebesar 82% dan berdasarkan hasil pengujian dengan data jenis kelamin didapatkan nilai akurasi tertinggi berada di batch 15 dengan epoch 50 sebesar 94%.

References

Arafat, M. Y., Hoque, S., Xu, S., & Farid, D. M. (2019). Advanced data balancing method with svm decision boundary and bagging. Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 1-7.

Azhari, M., Situmorang, Z., & Rosnelly, R. (2021). Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes. Jurnal Media Informatika Budidarma, 5(2), 640-651. http://dx.doi.org/10.30865/mib.v5i2.2937

Jena, M., Mishra, S. P., & Mishra, D. (2019). Empirical Analysis of Activation Functions and Pooling Layers in CNN For Classification of Diabetic Retinopathy. International Conference on Applied Machine Learning (ICAML), 34-39.

Karkkainen, K., & Joo, J. (2021). Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision,1548-1558.

Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep Learning Face Attributes in The Wild. Proceedings of the IEEE international conference on computer vision, 3730-3738.

Ma, D. S., Correll, J., & Wittenbrink, B. (2015). The Chicago Face Database: A Free Stimulus Set of Faces and Norming Data. Behavior Research Methods 47(4):1122–1135. 10.3758/s13428-014-0532-5.

Mahjabin, S., Alam, M. M., & Talukder, K. H. (2019). Age Estimation from Facial Image Using Convolutional Neural Network (CNN). International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2),1-4.

Mohammed, A. A., & Sajjanhar, A. (2017). Investigation of Gender and Race Classification for Different Color Models. International Conference on Digital Image Computing: Techniques and Applications (DICTA),1-8.

Mohammed, N. A., Abed, M. H., & Albu-Salih, A. T. (2022). Convolutional Neural Network for Color Images Classification. Bulletin of Electrical Engineering and Informatics, 11(3), 1343-1349. https://doi.org/10.11591/eei.v11i3.3730

Omer, H. K., Jalab, H. A., Hasan, A. M., & Tawfiq, N. E. (2019). Combination of Local Binary Pattern and Face Geometric Features for Gender Classification from Face Images. 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 158-161.

Oza, P., Sharma, P., Patel, S., Adedoyin, F., & Bruno, A. (2022). Image Augmentation Techniques for Mammogram Analysis. Journal of Imaging, 8(5), 141. https://doi.org/10.3390/jimaging8050141

Rahayu, W., & Wahyudi, E. (2017). Classical Test Theory of Innapropriate Index Scoreâ€™ S Accuracy Comparison Using Confusion Matrix Accuracy Proportion in Educational Measurement. IJER-INDONESIAN JOURNAL OF EDUCATIONAL REVIEW, 4(1), 84-92. https://doi.org/10.21009/IJER.04.01.08

Shaily, T., & Kala, S. (2020). Bacterial image classification using convolutional neural networks. 17th India Council International Conference (INDICON), 1-6.

Srinivas, N., Atwal, H., Rose, D. C., Mahalingam, G., Ricanek, K., & Bolme, D. S. (2017). Age, Gender, And Fine-Grained Ethnicity Prediction Using Convolutional Neural Networks for The East Asian Face Dataset. 12th International Conference on Automatic Face & Gesture Recognition (FG 2017), 953-960.

Wang, M., Deng, W., Hu, J., Tao, X., & Huang, Y. (2019). Racial Faces in The Wild: Reducing Racial Bias by Information Maximization Adaptation Network. Proceedings of the ieee/cvf international conference on computer vision, 692-702.

Weni, I., Utomo, P. E. P., Hutabarat, B. F., & Alfalah, M. (2021). Detection of Cataract Based on Image Features Using Convolutional Neural Networks. Indonesian Journal of Computing and Cybernetics Systems), 15(1), 75-86.

Zhang, Z., Song, Y., & Qi, H. (2017). Age progression/regression by conditional adversarial autoencoder. Proceedings of the IEEE conference on computer vision and pattern recognition, 5810-5818.

Zouleykha, K., Sabrina, B., Fayçal, A., & Abdelali, B. (2018). Traffic Sign Recognition and Classification Using Convolutional Neural Networks. International Journal of Computer Science Trends and Technology (IJCST), 6(2), 132-147.