Pengenalan Wajah Berdasarkan Emosi Manusia Menggunakan SOM (Self Organizing Map)
DOI:
https://doi.org/10.12928/jstie.v12i3.28620Keywords:
Identifikasi Biometrik, Ekspresi Wajah, Emosi Manusia, Keamanan Teknologi Biometrik, Self Organizing MapAbstract
Identifikasi melalui password atau kartu rentan terhadap lupa dan pencurian, menyebabkan keamanan yang kurang efektif. Sistem identifikasi biometrik, terutama berbasis ekspresi wajah, menjanjikan solusi lebih baik. Namun, tantangan seperti variabilitas ekspresi dan kondisi pencahayaan membatasi efisiensi. Penelitian ini mengusulkan penggunaan Self Organising Map (SOM) untuk mengatasi kendala tersebut. Meskipun telah ada penelitian sebelumnya, penggabungan pengenalan wajah dan emosi dengan SOM masih terbatas. Tujuan penelitian ini adalah mengembangkan sistem pengenalan wajah berdasarkan emosi manusia menggunakan pendekatan SOM. Pendekatan ini tidak hanya meningkatkan keamanan dan kenyamanan tetapi juga membuka peluang baru dalam interaksi manusia dan mesin, pengawasan keamanan, dan pengembangan teknologi sehari-hari. Dengan mengatasi keterbatasan identifikasi konvensional, penelitian ini memperluas potensi teknologi biometrik
References
[1] K. Amda and R. Fitriyani, Membaca ekspresi wajah. Penerbit Genesis, 2019.
[2] F. N. U. R. AZIZI, “Deteksi Emosi Menggunakan Citra Ekspresi Wajah Secara Otomatis,” 2021.
[3] A. S. Sitio and A. Sindar, “Sistem Identifikasi Biometrik Ekpresi Wajah Menggunakan Metode Transformasi Hough,” J. Nas. Komputasi dan Teknol. Inf., vol. 3, no. 3, 2020.
[4] I. P. Sari, F. Ramadhani, A. Satria, and D. Apdilah, “Implementasi Pengolahan Citra Digital dalam Pengenalan Wajah menggunakan Algoritma PCA dan Viola Jones,” Hello World J. Ilmu Komput., vol. 2, no. 3, pp. 146–157, 2023, doi: 10.56211/helloworld.v2i3.346.
[5] S. Helmiyah, I. Riadi, R. Umar, A. Hanif, A. Yudhana, and A. Fadlil, “Identifikasi Emosi Manusia Berdasarkan Ucapan Menggunakan Metode Ekstraksi Ciri LPC dan Metode Euclidean Distance,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 6, 2020.
[6] T. Kohonen, “Essentials of the self-organizing map,” Neural networks, vol. 37, pp. 52–65, 2013.
[7] J. Almotiri, “Face recognition using principal component analysis and clustered self-organizing map,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 3, 2022.
[8] T. Kohonen, “The self-organizing map,” Proc. IEEE, vol. 78, no. 9, pp. 1464–1480, 1990.
[9] L. V Fausett, Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India, 2006.
[10] M. Mittal and K. Kumar, “Energy efficient homogeneous wireless sensor network using self-organizing map (SOM) neural networks,” African J. Comput. & ICT, vol. 8, no. 1, pp. 179–184, 2015.
[11] K. Kumar, “Self-organizing map (SOM) neural networks for air space sectoring,” in 2014 International Conference on Computational Intelligence and Communication Networks, 2014, pp. 1096–1100.
[12] Z. Song and Z. Xia, “Carbon Emission Reduction of Tunnel Construction Machinery System Based on Self-Organizing Map-Global Particle Swarm Optimization With Multiple Weight Varying Models,” IEEE Access, vol. 10, p. 1, 2022, doi: 10.1109/ACCESS.2022.3173735.
[13] S. Haykin, Neural networks and learning machines, 3/E. Pearson Education India, 2009.
[14] S. N. Kapita, A. Mubarak, S. Do Abdullah, and M. Fhadli, “Penerapan Algoritma Clustering Khonen-Som Dengan Validasi Davies Bouldin Index Pada Pengelompokan Potensi Udang Di Indonesia,” IJIS-Indonesian J. Inf. Syst., vol. 7, no. 2, pp. 134–143, 2022.
[15] M. R. Berthold and F. Höppner, “On clustering time series using euclidean distance and pearson correlation,” arXiv Prepr. arXiv1601.02213, 2016.
[16] H. Ritter, T. Martinetz, and K. Schulten, Neural Computation and Self-Organizing Maps; An Introduction. Addison-Wesley Longman Publishing Co., Inc., 1992.
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