Modifikasi Dataset Fer-2013 Berdasarkan Augmentasi Data Dan Smote Untuk Klasifikasi Raut Wajah Menggunakan Convolutional Neural Network (CNN)
Kata Kunci:
klasifikasi raut wajah, augmentasi data, SMOTE, FER-2013, CK , MMA facial expresion, Convolutional Neural Network (CNN)Abstrak
Jurnal ini akan dipublikasikan pada Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Referensi
ABADI, M., AGARWAL, A., BARHAM, ... AND ZHENG, X., 2015. TensorFlow: Large-Scale machine learning on heterogeneous distributed systems. arXiv (Cornell University). [online] Available at: <http://datascienceassn.org/sites/default/files/TensorFlow%20-%20Large-Scale%20Machine%20Learning%20on%20Heterogeneous%20Distributed%20Systems.pdf>.
AMAANULLAH, R.R., PASFICA, G.R., NUGRAHA, S., ZEIN, M.R. AND ADHINATA, F.D., n.d. Implementasi Convolutional Neural Network Untuk Deteksi Emosi Melalui Wajah. JTIM : Jurnal Teknologi Informasi Dan Multimedia, [online] 3(4), pp.236–244. https://doi.org/10.35746/jtim.v3i4.189.
BOTTOU, L., CURTIS, F.E. AND NOCEDAL, J., 2018. Optimization Methods for Large-Scale Machine Learning. Siam Review, [online] 60(2), pp.223–311. https://doi.org/10.1137/16m1080173.
CHOLLET, F., 2017. Deep Learning with Python. [online] Available at: <http://cds.cern.ch/record/2301910>.
JOSEPH, J. AND MATHEW, S.P., 2021. Facial expression recognition for the blind using deep learning. 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON). [online] https://doi.org/10.1109/gucon50781.2021.9574035.
KESKAR, N.S., MUDIGERE, D., NOCEDAL, J., SMELYANSKIY, M. AND TANG, P., 2016. On Large-Batch training for deep learning: generalization gap and sharp minima. International Conference on Learning Representations. [online] Available at: <https://openreview.net/pdf?id=H1oyRlYgg>.
KHANZADA, A., 2020. Facial Expression Recognition with Deep Learning. [online] arXiv.org. Available at: <https://arxiv.org/abs/2004.11823>.
KUSUMA, G.P., JONATHAN, J. AND LIM, A.P., 2020. Emotion recognition on FER-2013 face images using Fine-Tuned VGG-16. Advances in Science, Technology and Engineering Systems Journal, [online] 5(6), pp.315–322. https://doi.org/10.25046/aj050638.
LECUN, Y., BENGIO, Y. AND HINTON, G.E., 2015. Deep learning. Nature, [online] 521(7553), pp.436–444. https://doi.org/10.1038/nature14539.
LUCEY, P., COHN, J.F., KANADE, T., SARAGIH, J., AMBADAR, Z. AND MATTHEWS, I., 2010. The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops. [online] https://doi.org/10.1109/cvprw.2010.5543262.
MUSA, P., ANAM, W.K., MUSA, S.B., ARYUNANI, W., SENJAYA, R. AND SULARSIH, P., 2023b. Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network. Rekayasa: Jurnal Ilmiah Ilmu-Ilmu Eksakta Dan Teknologi, [online] 16(1), pp.65–73. https://doi.org/10.21107/rekayasa.v16i1.16974.
NDUN, R. IMANUEL, 2020. Mendeteksi jenis burung berdasarkan gambar menggunakan Deep Learning. https://repository.dinamika.ac.id/.
NUR, I. T., SETIAWAN, N. Y., & BACHTIAR, F. A., 2019. Perbandingan Performa Metode Klasifikasi SVM, Neural Network, Dan Naive Bayes Untuk Mendeteksi Kualitas Pengajuan Kredit Di Koperasi Simpan Pinjam. Jurnal Teknologi Informasi dan Ilmu Komputer, 6, 445-450. doi:10.25126/jtiik.201961352
PINANDITO, A., WICAKSONO, S.A. AND WIJOYO, S.H., 2023. Implementasi Machine Learning dalam Deteksi Risiko Tinggi Diabetes Melitus pada Kehamilan. Jurnal Teknologi Informasi Dan Ilmu Komputer, [online] 10(4), pp.739–746. https://doi.org/10.25126/jtiik.20241047005.
RAMDHANI, B., DJAMAL, E.C. AND ILYAS, R., 2018. Convolutional Neural Networks Models for Facial Expression Recognition. Proceeding - 2018 International Symposium on Advanced Intelligent Informatics: Revolutionize Intelligent Informatics Spectrum for Humanity, SAIN 2018. [online] https://doi.org/10.1109/sain.2018.8673352.
SHORTEN, C. AND KHOSHGOFTAAR, T.M., 2019. A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, [online] 6(1). https://doi.org/10.1186/s40537-019-0197-0.
SRIVASTAVA, N., HINTON, G.E., KRIZHEVSKY, A., SUTSKEVER, I. AND SALAKHUTDINOV, R., 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, [online] 15(1), pp.1929–1958. Available at: <https://jmlr.csail.mit.edu/papers/volume15/srivastava14a/srivastava14a.pdf>.
UTAMI, H., 2022. Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network. Indonesian Journal of Applied Statistics, [online] 5(1), p.31. https://doi.org/10.13057/ijas.v5i1.56825.
ZHANG, H., ZHANG, L. AND JIANG, Y., 2019. Overfitting and Underfitting Analysis for Deep Learning Based End-to-end Communication Systems. 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). [online] https://doi.org/10.1109/wcsp.2019.8927876.
ABADI, M., AGARWAL, A., BARHAM, ... AND ZHENG, X., 2015. TensorFlow: Large-Scale machine learning on heterogeneous distributed systems. arXiv (Cornell University). [online] Available at: <http://datascienceassn.org/sites/default/files/TensorFlow%20-%20Large-Scale%20Machine%20Learning%20on%20Heterogeneous%20Distributed%20Systems.pdf>.
AMAANULLAH, R.R., PASFICA, G.R., NUGRAHA, S., ZEIN, M.R. AND ADHINATA, F.D., n.d. Implementasi Convolutional Neural Network Untuk Deteksi Emosi Melalui Wajah. JTIM : Jurnal Teknologi Informasi Dan Multimedia, [online] 3(4), pp.236–244. https://doi.org/10.35746/jtim.v3i4.189.
BOTTOU, L., CURTIS, F.E. AND NOCEDAL, J., 2018. Optimization Methods for Large-Scale Machine Learning. Siam Review, [online] 60(2), pp.223–311. https://doi.org/10.1137/16m1080173.
CHOLLET, F., 2017. Deep Learning with Python. [online] Available at: <http://cds.cern.ch/record/2301910>.
JOSEPH, J. AND MATHEW, S.P., 2021. Facial expression recognition for the blind using deep learning. 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON). [online] https://doi.org/10.1109/gucon50781.2021.9574035.
KESKAR, N.S., MUDIGERE, D., NOCEDAL, J., SMELYANSKIY, M. AND TANG, P., 2016. On Large-Batch training for deep learning: generalization gap and sharp minima. International Conference on Learning Representations. [online] Available at: <https://openreview.net/pdf?id=H1oyRlYgg>.
KHANZADA, A., 2020. Facial Expression Recognition with Deep Learning. [online] arXiv.org. Available at: <https://arxiv.org/abs/2004.11823>.
KUSUMA, G.P., JONATHAN, J. AND LIM, A.P., 2020. Emotion recognition on FER-2013 face images using Fine-Tuned VGG-16. Advances in Science, Technology and Engineering Systems Journal, [online] 5(6), pp.315–322. https://doi.org/10.25046/aj050638.
LECUN, Y., BENGIO, Y. AND HINTON, G.E., 2015. Deep learning. Nature, [online] 521(7553), pp.436–444. https://doi.org/10.1038/nature14539.
LUCEY, P., COHN, J.F., KANADE, T., SARAGIH, J., AMBADAR, Z. AND MATTHEWS, I., 2010. The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops. [online] https://doi.org/10.1109/cvprw.2010.5543262.
MUSA, P., ANAM, W.K., MUSA, S.B., ARYUNANI, W., SENJAYA, R. AND SULARSIH, P., 2023b. Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network. Rekayasa: Jurnal Ilmiah Ilmu-Ilmu Eksakta Dan Teknologi, [online] 16(1), pp.65–73. https://doi.org/10.21107/rekayasa.v16i1.16974.
NDUN, R. IMANUEL, 2020. Mendeteksi jenis burung berdasarkan gambar menggunakan Deep Learning. https://repository.dinamika.ac.id/.
NUR, I. T., SETIAWAN, N. Y., & BACHTIAR, F. A., 2019. Perbandingan Performa Metode Klasifikasi SVM, Neural Network, Dan Naive Bayes Untuk Mendeteksi Kualitas Pengajuan Kredit Di Koperasi Simpan Pinjam. Jurnal Teknologi Informasi dan Ilmu Komputer, 6, 445-450. doi:10.25126/jtiik.201961352
PINANDITO, A., WICAKSONO, S.A. AND WIJOYO, S.H., 2023. Implementasi Machine Learning dalam Deteksi Risiko Tinggi Diabetes Melitus pada Kehamilan. Jurnal Teknologi Informasi Dan Ilmu Komputer, [online] 10(4), pp.739–746. https://doi.org/10.25126/jtiik.20241047005.
RAMDHANI, B., DJAMAL, E.C. AND ILYAS, R., 2018. Convolutional Neural Networks Models for Facial Expression Recognition. Proceeding - 2018 International Symposium on Advanced Intelligent Informatics: Revolutionize Intelligent Informatics Spectrum for Humanity, SAIN 2018. [online] https://doi.org/10.1109/sain.2018.8673352.
SHORTEN, C. AND KHOSHGOFTAAR, T.M., 2019. A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, [online] 6(1). https://doi.org/10.1186/s40537-019-0197-0.
SRIVASTAVA, N., HINTON, G.E., KRIZHEVSKY, A., SUTSKEVER, I. AND SALAKHUTDINOV, R., 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, [online] 15(1), pp.1929–1958. Available at: <https://jmlr.csail.mit.edu/papers/volume15/srivastava14a/srivastava14a.pdf>.
UTAMI, H., 2022. Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network. Indonesian Journal of Applied Statistics, [online] 5(1), p.31. https://doi.org/10.13057/ijas.v5i1.56825.
ZHANG, H., ZHANG, L. AND JIANG, Y., 2019. Overfitting and Underfitting Analysis for Deep Learning Based End-to-end Communication Systems. 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). [online] https://doi.org/10.1109/wcsp.2019.8927876.
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2024 Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Artikel ini berlisensiCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
