COMPARISON OF SGD, ADADELTA, ADAM OPTIMIZATION IN GENDER CLASSIFICATION USING CNN

Sartika Mandasari, Desi Irfan, Wanayumini Wanayumini, Rika Rosnelly

Abstract


Abstract: Gender classification is one of the most important tasks of video analysis. A machine learning-based approach was presented to identify male and female facial images with a data set of 2000 images taken from kaggles.  This method plays a role in finding the weight value that gives the best output value. This study uses the most appropriate learning rate of each optimization method as a criterion for stopping training. The results showed that the Artificial Neural Network with Adam optimization produced the highest accuracy, which was 91.5% compared to the SGD and ADADELTA optimization methods. Deep Learning techniques that are applied extensively to image recognition used utilize Adam's optimizer method.     

Keywords: artificial neural networks; adadelta; adam; gender; sgm;

 

 

Abstrak: Klasifikasi gender adalah salah satu tugas analisis video yang paling penting. Pendekatan berbasis machine learning disajikan untuk mengidentifikasi gambar wajah Pria dan Wanita dengan kumpulan data sebanyak 2000 gambar yang diambil dari kaggle.  Metode ini berperan dalam menemukan nilai bobot yang memberikan nilai keluaran terbaik. Penelitian ini menggunakan learning rate yang paling sesuai dari masing-masing metode optimasi sebagai kriteria pemberhentian pelatihan. Hasil penelitian menunjukkan Jaringan Saraf Tiruan dengan optimasi Adam menghasilkan akurasi tertinggi yaitu 91,5 %  dibandingkan dengan dengan metode optimasi SGD dan ADADELTA. Teknik Deep Learning yang diterapkan secara ekstensif pada pengenalan gambar yang digunakan memanfaatkan metode optimizer Adam.

 

Kata kunci: Adadelta; Adam; Jaringan Syaraf; Gender; Tiruan; SGM;

 


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References


J. Ho et al., “Imagen Video: High Definition Video Generation with Diffusion Models,” pp. 1–18, 2022, [Online]. Available: http://arxiv.org/abs/2210.02303

F. D. Adinata and J. Arifin, “Klasifikasi Jenis Kelamin Wajah Bermasker Menggunakan Algoritma Supervised Learning,” J. Media Inform. Budidarma, vol. 6, no. 1, p. 229, 2022, doi: 10.30865/mib.v6i1.3377.

A. M. Rizki, G. E. Yuliastuti, E. Y. Puspaningrum, and A. Lina, “KLASIFIKASI JENIS KELAMIN BERDASARKAN CIRI FISIK,” vol. XVII, pp. 1–5, 2022.

C. Neural, N. Cnn, R. Firdaus, and J. Satria, “Jurnal Computer Science and Information Technology ( CoSciTech ) Klasifikasi Jenis Kelamin Berdasarkan Gambar Mata Dengan Menggunakan Algoritma,” vol. 3, no. 3, pp. 267–273, 2022.

Y. Dong, Q. Liu, B. Du, and L. Zhang, “Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network for Hyperspectral Image Classification,” IEEE Trans. Image Process., vol. 31, no. January, pp. 1559–1572, 2022, doi: 10.1109/TIP.2022.3144017.

D. Irfan, R. Rosnelly, M. Wahyuni, J. T. Samudra, and A. Rangga, “Perbandingan Optimasi Sgd, Adadelta, Dan Adam Dalam Klasifikasi Hydrangea Menggunakan Cnn,” J. Sci. Soc. Res., vol. 5, no. 2, p. 244, 2022, doi: 10.54314/jssr.v5i2.789.

D. Frenza and R. Mukhaiyar, “Aplikasi Pengenalan Wajah Menggunakan Metode Adaptive Resonance Theory ( ART ),” Multidicsiplinary Res. Dev., vol. 3, no. 1, pp. 35–42, 2021, [Online]. Available: https://doi.org/10.31933/rrj.v3i3.392

M. Ula, A. Faridhatul Ulva, M. Abdullah Ali, Y. Rilasmi Said, and S. Informasi, “Application of Machine Learning in Determining the Classification of Children’S Nutrition With Decision Tree,” J. Tek. Inform., vol. 3, no. 5, pp. 1457–1465, 2022, [Online]. Available: https://doi.org/10.20884/1.jutif.2022.3.5.599

S. W. P. Listio, “Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image,” Sinkron, vol. 7, no. 4, pp. 2593–2601, 2022, doi: 10.33395/sinkron.v7i4.11887.

B. Hardiansyah and A. Primasetya, “Sistem Deteksi Penggunaan masker ( Face Mask Detection ) Menggunakan Algoritma Deep Learning YOLOv4,” vol. 2, pp. 313–318, 2023.

S. H. Abdullah, R. Magdalena, and R. Y. N. Fu’adah, “Klasifikasi Diabetic Retinopathy Berbasis Pengolahan Citra Fundus Dan Deep Learning,” J. Electr. Syst. Control Eng., vol. 5, no. 2, pp. 84–90, 2022, doi: 10.31289/jesce.v5i2.5659.

Bimrew Sendekie Belay, “No Titleהכי קשה לראות את מה שבאמת לנגד העינים,” הארץ, vol. 7, no. 8.5.2017, pp. 2003–2005, 2022.

HAMDANI MUBAROK, “Menggunakan Algoritma Convolutional Neural Network (CNN),” Skripsi, vol. 4, no. 2, pp. 89–96, 2019.

D. Hardiyanto and D. Anggun Sartika, “Optimalisasi Metode Deteksi Wajah berbasis Pengolahan Citra untuk Aplikasi Identifikasi Wajah pada Presensi Digital,” Setrum Sist. Kendali-Tenaga-elektronika-telekomunikasi-komputer, vol. 7, no. 1, p. 107, 2018, doi: 10.36055/setrum.v7i1.3367.

F. Mignacco and P. Urbani, “The effective noise of stochastic gradient descent,” J. Stat. Mech. Theory Exp., vol. 2022, no. 8, pp. 1–15, 2022, doi: 10.1088/1742-5468/ac841d.

Y. Kong, X. Ma, and C. Wen, “A New Method of Deep Convolutional Neural Network Image Classification Based on Knowledge Transfer in Small Label Sample Environment,” Sensors, vol. 22, no. 3, 2022, doi: 10.3390/s22030898.

R. Rosnelly, L. Wahyuni, and E. Aditya, “Pelatihan Pengenalan Teknik Pengolahan Citra Digital Pada Bidang Medis Training Introduction to Digital Image Processing Techniques In The Medical Field,” JUDIMAS J. Inov. Pengabdi. Kpd. Masy., vol. 3, no. 1, pp. 11–19, 2022, [Online]. Available: https://stmikpontianak.ac.id/ojs/index.php/judimas/article/view/1282

K. C. Leowis, J. Raharjo, N. Ibrahim, U. Telkom, F. Detecion, and P. C. Analysis, “Rancang Bangun Sistem Pengenalan Wajah Diarea Publik Berbasis Video Menggunakan Metode Principal Component Analysis ( Pca ) Dan Viola Jones Design of the Public Face Recognition System Based on Video Using Principal Component Analysis ( Pca ) and Viola Jo,” vol. 8, no. 5, pp. 5448–5464, 2021.

F. Tangguh and Y. Islami, “Analisis performa algoritma Stochastic Gradient Descent ( SGD ) dalam mengklasifikasi tahu berformalin,” Indones. J. Data Sci., vol. 3, no. 1, pp. 1–8, 2022.

M. Milano, G. Agapito, and M. Cannataro, “Application of CCTV Methodology to Analyze COVID-19 Evolution in Italy,” BioTech, vol. 11, no. 3, pp. 1–22, 2022, doi: 10.3390/biotech11030033.

Z. Lou, W. Zhu, and W. B. Wu, “Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent,” J. Mach. Learn. Res., vol. 23, pp. 1–22, 2022.




DOI: https://doi.org/10.33330/jurteksi.v9i3.2067

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