Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

Authors

Syafrika Deni Rizki , Sarjon Defit

DOI:

10.29303/jppipa.v10i5.7146

Published:

2024-05-25

Issue:

Vol. 10 No. 5 (2024): May

Keywords:

Artificial neural networks, Backpropagation, Flags, Texture extraction

Research Articles

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How to Cite

Rizki, S. D., & Defit, S. (2024). Texture and Flag Color Extraction in Backpropagation Neural Network Architecture. Jurnal Penelitian Pendidikan IPA, 10(5), 2191–2198. https://doi.org/10.29303/jppipa.v10i5.7146
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Abstract

A flag is a rectangular or triangular piece of cloth or paper used as a symbol of the state, association, body, etc. or as a sign. It is often also used to symbolize a country to show its sovereignty. Along with the large number of countries, the country's flag also has many varieties and colors. The use of computers as a human aid is expected to the extent that the computer's ability can replace the limitations that humans have. Humans can recognize an object by using their eyes and brain, but if the eyes and brain cannot work properly it will hamper human work. In this research, training will be conducted on the Back Propagation Neural Network Architecture. Characteristic data for image recognition is obtained by extracting texture features and RGB color features. So that the network can recognize the flags by matching the feature data obtained from the training carried out. Characteristic data obtained from 24 data consisting of 16 training images and 8 testing images. From the results of the image network training can be identified properly, the accuracy rate of object identification is 87.50%. GUI users are able to identify flag images based on RGB text and color features.

References

Aisyah, S., Harahap, M., Husein Siregar, A. M., & Turnip, M. (2018). Optimization of training backpropagation algorithm using nguyen widrow for angina ludwig diagnosis. Journal of Physics: Conference Series, 1007, 012050. https://doi.org/10.1088/1742-6596/1007/1/012050

Al Ghamdi, M. A. (2023). Analyze textual data: Deep neural network for adversarial inversion attack in wireless networks. SN Applied Sciences, 5(12), 386. https://doi.org/10.1007/s42452-023-05565-8

Becker, J. C., Butz, D. A., Sibley, C. G., Barlow, F. K., Bitacola, L. M., Christ, O., Khan, S. S., Leong, C.-H., Pehrson, S., Srinivasan, N., Sulz, A., Tausch, N., Urbanska, K., & Wright, S. C. (2017). What Do National Flags Stand for? An Exploration of Associations Across 11 Countries. Journal of Cross-Cultural Psychology, 48(3), 335–352. https://doi.org/10.1177/0022022116687851

Benmouna, B., García-Mateos, G., Sabzi, S., Fernandez-Beltran, R., Parras-Burgos, D., & Molina-Martínez, J. M. (2022). Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy. Food and Bioprocess Technology, 15(10), 2226–2236. https://doi.org/10.1007/s11947-022-02880-7

Blake, A. B., & Castel, A. D. (2019). Memory and availability-biased metacognitive illusions for flags of varying familiarity. Memory & Cognition, 47(2), 365–382. https://doi.org/10.3758/s13421-018-0872-y

Chen, X., Pang, T., Tao, H., Lin, M., & Yang, H. (2017). Prediction of apple internal qualities using hyperspectral imaging techniques. 2017 International Conference on Advanced Mechatronic Systems (ICAMechS), 450–455. https://doi.org/10.1109/ICAMechS.2017.8316516

Cruz-Lopez, J. A., Boyer, V., & El-Baz, D. (2017). Training Many Neural Networks in Parallel via Back-Propagation. 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 501–509. https://doi.org/10.1109/IPDPSW.2017.72

Dalal, S., Manoharan, P., Lilhore, U. K., Seth, B., Mohammed Alsekait, D., Simaiya, S., Hamdi, M., & Raahemifar, K. (2023). Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment. Journal of Cloud Computing, 12(1), 14. https://doi.org/10.1186/s13677-022-00356-9

Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023). A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discover Artificial Intelligence, 3(1), 43. https://doi.org/10.1007/s44163-023-00089-x

Fachri, B., Windarto, A. P., & Parinduri, I. (2019). Penerapan Backpropagation dan Analisis Sensitivitas pada Prediksi Indikator Terpenting Perusahaan Listrik. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(2), 202. https://doi.org/10.26418/jp.v5i2.31650

Fadlil, A., Umar, R., & Gustina, S. (2019). Mushroom Images Identification Using Orde 1 Statistics Feature Extraction with Artificial Neural Network Classification Technique. Journal of Physics: Conference Series, 1373(1), 012037. https://doi.org/10.1088/1742-6596/1373/1/012037

Goh, T. Y., Basah, S. N., Yazid, H., Aziz Safar, M. J., & Ahmad Saad, F. S. (2018). Performance analysis of image thresholding: Otsu technique. Measurement, 114, 298–307. https://doi.org/10.1016/j.measurement.2017.09.052

Korteling, J. E. (Hans)., Van De Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021). Human- versus Artificial Intelligence. Frontiers in Artificial Intelligence, 4, 622364. https://doi.org/10.3389/frai.2021.622364

Kreyenmeier, P., Kämmer, L., Fooken, J., & Spering, M. (2021). Humans can track but fail to predict accelerating objects [Preprint]. Neuroscience. https://doi.org/10.1101/2021.11.20.469397

Liu, C., & Xu, B. (2023). Weakly-supervised structural surface crack detection algorithm based on class activation map and superpixel segmentation. Advances in Bridge Engineering, 4(1), 27. https://doi.org/10.1186/s43251-023-00106-0

Ma, J., Qian, K., Zhang, X., & Ma, X. (2020). Weakly Supervised Instance Segmentation of Electrical Equipment Based on RGB-T Automatic Annotation. IEEE Transactions on Instrumentation and Measurement, 69(12), 9720–9731. https://doi.org/10.1109/TIM.2020.3001796

Manakitsa, N., Maraslidis, G. S., Moysis, L., & Fragulis, G. F. (2024). A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision. Technologies, 12(2), 15. https://doi.org/10.3390/technologies12020015

Medway, D., Light, D., Warnaby, G., Byrom, J., & Young, C. (2019). Flags, society and space: Towards a research agenda for vexillgeography. Area, 51(4), 689–696. https://doi.org/10.1111/area.12516

Naaz, S., Khan, S. A., Siddiqui, F., Sohail, S. S., Madsen, D. Ø., & Ahmad, A. (2022). OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor. International Journal of Environmental Research and Public Health, 19(24), 16777. https://doi.org/10.3390/ijerph192416777

Nofitriyani, N., Wahid, F., & Pratama, A. R. (2022). Online Learning System Acceptance by Indonesian High School Students During the Covid-19 Pandemic with Utaut. Jurnal Teknik Informatika (Jutif), 3(6), 1533–1538. https://doi.org/10.20884/1.jutif.2022.3.3.347

Putra, H., & Ulfa Walmi, N. (2020). Penerapan Prediksi Produksi Padi Menggunakan Artificial Neural Network Algoritma Backpropagation. Jurnal Nasional Teknologi Dan Sistem Informasi, 6(2), 100–107. https://doi.org/10.25077/TEKNOSI.v6i2.2020.100-107

Salah Alaloul, W., & Hannan Qureshi, A. (2020). Data Processing Using Artificial Neural Networks. In D. G. Harkut (Ed.), Dynamic Data Assimilation—Beating the Uncertainties. IntechOpen. https://doi.org/10.5772/intechopen.91935

Sarfraz, M. (2020). Introductory Chapter: On Digital Image Processing. In M. Sarfraz (Ed.), Digital Imaging. IntechOpen. https://doi.org/10.5772/intechopen.92060

Sarker, I. H. (2021). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science, 2(6), 420. https://doi.org/10.1007/s42979-021-00815-1

Senthilkumaran, N., & Rajesh, R. (2009). Image Segmentation—A Survey of Soft Computing Approaches. 2009 International Conference on Advances in Recent Technologies in Communication and Computing, 844–846. https://doi.org/10.1109/ARTCom.2009.219

Shafiq, M., & Gu, Z. (2022). Deep Residual Learning for Image Recognition: A Survey. Applied Sciences, 12(18), 8972. https://doi.org/10.3390/app12188972

Silaban, H., Zarlis, M., & Sawaluddin. (2017). Analysis of Accuracy and Epoch on Back-propagation BFGS Quasi-Newton. Journal of Physics: Conference Series, 930, 012006. https://doi.org/10.1088/1742-6596/930/1/012006

Sun, T., Wei, Y., Chen, W., & Ding, Y. (2020). Genome‐wide association study‐based deep learning for survival prediction. Statistics in Medicine, 39(30), 4605–4620. https://doi.org/10.1002/sim.8743

Suyadi, S., & Sabiq, A. F. (2021). Classical Islamic Political Thought: Study of Sunni Perspectives. Islah: Journal of Islamic Literature and History, 1(2), 137–153. https://doi.org/10.18326/islah.v1i2.137-153

Taner, A., Mengstu, M. T., Selvi, K. Ç., Duran, H., Gür, İ., & Ungureanu, N. (2024). Apple Varieties Classification Using Deep Features and Machine Learning. Agriculture, 14(2), 252. https://doi.org/10.3390/agriculture14020252

Taye, M. M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12(5), 91. https://doi.org/10.3390/computers12050091

Trunk, A., Birkel, H., & Hartmann, E. (2020). On the current state of combining human and artificial intelligence for strategic organizational decision making. Business Research, 13(3), 875–919. https://doi.org/10.1007/s40685-020-00133-x

Ukwuoma, C. C., Zhiguang, Q., Bin Heyat, M. B., Ali, L., Almaspoor, Z., & Monday, H. N. (2022). Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques. Mathematical Problems in Engineering, 1–29. https://doi.org/10.1155/2022/9210947

Vadivelu, R., Santhakumar, G., Kaviya, P., Menaka, M., Monisha, S., & Balasubramaniam, D. (2021). Human Body Communication on Portable Biometric Authentication. Journal of Physics: Conference Series, 1916(1), 012113. https://doi.org/10.1088/1742-6596/1916/1/012113

Weng, Q., Wang, Q., Lin, Y., & Lin, J. (2023). ARE-Net: An Improved Interactive Model for Accurate Building Extraction in High-Resolution Remote Sensing Imagery. Remote Sensing, 15(18), 4457. https://doi.org/10.3390/rs15184457

Wildan, T. (2023). Principles of the teaching of nation and state life in the Constitution of Medina. IBDA` : Jurnal Kajian Islam Dan Budaya, 21(1), 17–36. https://doi.org/10.24090/ibda.v21i1.6747

Wiley, V., & Lucas, T. (2018). Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelligence Research, 2(1), 22. https://doi.org/10.29099/ijair.v2i1.42

Wu, Y., He, Y., & Wang, Y. (2023). Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier. Sensors, 23(16), 7153. https://doi.org/10.3390/s23167153

Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: An overview and application in radiology. Insights into Imaging, 9(4), 611–629. https://doi.org/10.1007/s13244-018-0639-9

Zhuk, A. (2023). Virtual Sovereignty: Examining the Legal Status of Micronations in Cyberspace Through the Case of the Republic of Errant Menda Lerenda. Digital Society, 2(3), 45. https://doi.org/10.1007/s44206-023-00067-x

Author Biographies

Syafrika Deni Rizki, Universitas Putra Indonesia YPTK

Sarjon Defit, Universitas Putra Indonesia YPTK, Padang

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