Deteksi Konten Pornografi Menggunakan Convolutional Neural Network Untuk Melindungi Anak Dari Bahaya Pornografi
DOI:
https://doi.org/10.30865/mib.v6i4.4793Keywords:
Computer Vision, CNN, Pornography, Technology, Education, Transfer LearningAbstract
Education is one thing that must be arranged as early as conceivable in arrange to realize a quality era. When talking about education today, it cannot be separated from technology. Where we can see that technology has been used in various fields. In the field of education, one of them is the use of the internet network. However, the use of this technology has quite a bad side. Especially for elementary-level students or the age of children. That is the bad impact of exposure to pornography. Exposure to pornography is very dangerous and can damage children both psychologically and mentally. Therefore, it is important to minimize the risk of exposure to pornography. To overcome this, there are many methods that can be used. Like detecting pornographic content automatically and blocking it. One technique that can be developed to detect pornographic content is Artificial Neural Networks. However, so that the image input can be handled effectively, the model of the Artificial Neural Network has been varied into a Convolutional Neural Network (CNN) technique. So it has the ability to recognize objects for image data. The model built in this study was trained using a dataset that has been adapted to the definition of pornography in Indonesia. From the tests that have been carried out on the CNN model that was built, the best accuracy rate is 94.24%. in detecting images that fall into the category of pornographic content.References
R. Murni, Dampak media berkonten pornografi terhadap anak, Cetakan I. Cawang, Jakarta: Pusat Penelitian dan Pengembangan Kesejahteraan Sosial, Badan Pendidikan, Penelitian, dan Penyuluhan Sosial, Kementerian Sosial RI, 2018.
S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms, 1st ed. Cambridge University Press, 2014. doi: 10.1017/CBO9781107298019.
M. Zufar and B. Setiyono, “Convolutional Neural Networks untuk Pengenalan Wajah Secara Real-Time,†Jurnal Sains dan Seni, vol. 5, no. 2, pp. 72–77, 2016.
H. Abhirawa, M. Si, and A. Arifianto, “Pengenalan Wajah Menggunakan Convolutional Neural Network,†p. 10.
T. Nurhikmat, “IMPLEMENTASI DEEP LEARNING UNTUK IMAGE CLASSIFICATION MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) PADA CITRA WAYANG GOLEK,†p. 113.
W. Zhang, C. Li, G. Peng, Y. Chen, and Z. Zhang, “A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load,†Mechanical Systems and Signal Processing, vol. 100, pp. 439–453, Feb. 2018, doi: 10.1016/j.ymssp.2017.06.022.
S. Hijazi, R. Kumar, and C. Rowen, “Using Convolutional Neural Networks for Image Recognition,†p. 12.
Y. Hong and J. Kim, “A 2D-View Depth Image- and CNN-Based 3D Model Identification Method,†Applied Sciences, vol. 7, no. 10, p. 988, Sep. 2017, doi: 10.3390/app7100988.
D. Nasution, T. H. F. Harumy, E. Haryanto, F. Fachrizal, Julham, and A. Turnip, “A classification method for prediction of qualitative properties of multivariate EEG-P300 signals,†in 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), Bandung, Indonesia, Oct. 2015, pp. 82–86. doi: 10.1109/ICACOMIT.2015.7440180.
O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,†Int J Comput Vis, vol. 115, no. 3, pp. 211–252, Dec. 2015, doi: 10.1007/s11263-015-0816-y.
A. Hunaepi, M. Makhsun, and S. Sarwani, “DETEKSI SITUS PORNOGRAFI BERDASARKAN GAMBAR MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK,†J. Teknik Informatika, vol. 12, no. 2, pp. 137–148, Nov. 2019, doi: 10.15408/jti.v12i2.12488.
M. Moustafa, “Applying deep learning to classify pornographic images and videos.†arXiv, Nov. 28, 2015. Accessed: Sep. 01, 2022. [Online]. Available: http://arxiv.org/abs/1511.08899
S. Winiarti, C. Wukir, U. Ahdiani, and T. Ismail, “Klasifikasi Image Untuk Jenis Buku Bacaan Anak-Anak dengan Menggunakan Convolutional Neural Network,†vol. 6, p. 8, 2022.
R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,†Insights Imaging, vol. 9, no. 4, pp. 611–629, Aug. 2018, doi: 10.1007/s13244-018-0639-9.
L. Marifatul Azizah, S. Fadillah Umayah, and F. Fajar, “Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer,†st, vol. 21, no. 2, 2018, doi: 10.18196/st.212229.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org
T. Bariyah, M. A. Rasyidi, and N. Ngatini, “Convolutional Neural Network untuk Metode Klasifikasi Multi-Label pada Motif Batik,†tc, vol. 20, no. 1, pp. 155–165, Feb. 2021, doi: 10.33633/tc.v20i1.4224.
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