Skip to main content
Article | Green Intelligent Systems and Applications | Volume 3 - Issue 2 - 2023 | 56-68 | https://doi.org/10.53623/gisa.v3i2.270
© 2023 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Application of Convolutional Neural Network (CNN) Method in Fluctuations Pattern

Author(s): Melinda Melinda 1 , Yunidar Yunidar 1 , Nur Afny Catur Andryani 2
Author(s) information:
1 Department of Electrical Engineering and Computer, Engineering Faculty, Universitas Syiah Kuala, Banda Aceh, Indonesia
2 Computer Science Program, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia

Corresponding author

Green Intelligent Systems and Applications

Volume 3 - Issue 2 - 2023

 About the journal
Submit your article

In the acquisition of amplitude data, the inaccuracy of a signal with the occurrence of an unstable peak value of the amplitude in the data is called a fluctuation. This study uses High-High Fluctuation (HHF) signal data from the acquisition of Multi-Spectral Capacitive Sensors (MSCS) with Hydrogen Dioxide (H2O) and Hydrogen Dioxide (H2O) objects mixed with Sodium Hydroxide (NaOH) that have been organized into a matrix form. The data acquisition results in previous studies have several parts that are difficult to distinguish with the naked eye. The method used in this study applies the CNN method for image recognition of signal fluctuations of type HHF from H2O and H2O mixed with NaOH, using the architecture known as AlexNet. Then, the H2O and H2O data groups mixed with NaOH were grouped into training data groups of 280 image data for each data type, and 70 image data for test data for both groups. During the training stage, the number of epochs used is 20. However, by the time the number of epochs reaches 15, the accuracy rate is already high, reaching 98%. Furthermore, at the testing stage, the CNN program can correctly recognize the entire 70 image data for both materials, achieving perfect recognition for the total amount of the two materials.

Read Full-Text
Previous article
Next article

Syahrial, S.; Melinda, M.; Irhamsyah, M.; Sheiba, T. (2022). Performance of Elliptic Filter on Multi-Spectral Signal Based on Divided Zone. Proceedings of the International Conference on Electrical Engineering and Informatics, pp. 178–183. http://doi.org/10.1109/ICELTICs56128.2022.9932079.

Melinda, M.; Tamsir, A. S.; Bastian, A.; Gunawan, D. (2018). Comparison Analysis of Data Groupings of Fluctuation Patterns based on the Amplitude Representation Value (ARV). Proceedings of the 2nd 2018 International Conference on Electrical Engineering and Informatics, ICELTICs 2018, pp. 25–29. http://doi.org/10.1109/ICELTICS.2018.8548853.

Melinda, M.; Sianturi, P.; Tamsir, A. S. (2019). Comparative Analysis of Material Fluctuation Response based on Data Set Groups. IOP Conference Series: Materials Science and Engineering, 620, 012092. http://doi.org/10.1088/1757-899X/620/1/012092.

Irhamsyah, M.; Melinda, M.; Yunidar, Y.; Syahrial, S. (2022). Parameters Quality Performance of Signal Fluctuation Based on Data Grouping. Proceedings of the 2022 International Conference on Electrical Engineering, Computer, and Information Technology, ICEECIT 2022, pp. 226–231. http://doi.org/10.1109/ICEECIT55908.2022.10030593.

Liu, C.; Zhou, X.; Zhou, Y.; Akbar, A. (2020). Multi-temporal monitoring of urban river water quality using UAV-borne multi-spectral remote sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43, pp. 1469–1475. http://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1469-2020.

Melinda, M.; Tanjung, A.; Tamsir, A. S.; Basari, B.; Gunawan, D. (2017). Grouped data analysis of H2O and H2O mixed with NaOH on multi-spectral high fluctuation pattern. Proceedings of the 2017 International Conference on Electrical Engineering, Informatics, Advanced Knowledge, Research, Technology, and Humanities - ICELTICs 2017, pp. 184–188. http://doi.org/10.1109/ICELTICS.2017.8253266.

Tamsir, A. S.; Melinda, M.; Hariadi, M.; Basari, B.; Gunawan, D. (2017). Self-comparison performance analysis of H2O on multi-spectral fluctuation pattern. Proceedings of the 2017 International Conference on Electrical Engineering, Informatics, Advanced Knowledge, Research, Technology, and Humanities - ICELTICs 2017, pp. 263–268. http://doi.org/10.1109/ICELTICS.2017.8253273.

Melinda, M.; Tamsir, A.S.; Afifah, S.; Gunawan, D. (2018). Impedance Influence Analysis on Multi Spectral Capacitive Sensor. Proceedings of the 2nd 2018 International Conference on Electrical Engineering and Informatics - ICELTICs 2018, pp. 19–24. http://doi.org/10.1109/ICELTICS.2018.8548794.

Dewa, C.K.; Fadhilah, A.L.; Afiahayati, A. (2018). Convolutional Neural Networks for Handwritten Javanese Character Recognition. Indonesian Journal of Computer and Cybernetics Systems, 12, 83‒94. http://doi.org/10.22146/ijccs.31144.

Dzulqarnain, M.F.; Suprapto, S.; Makhrus, F. (2019). Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image. Indonesian Journal of Computer and Cybernetics Systems, 13, 189‒198. http://doi.org/10.22146/ijccs.42036.

Chauhan, R.; Ghanshala, K.K.; Joshi, R.C. (2018). Convolutional Neural Network (CNN) for Image Detection and Recognition. 1st International Conference on Security, Cybernetics, and Communications - ICSCCC 2018, pp. 278–282. http://doi.org/10.1109/ICSCCC.2018.8703316.

Hassan, R.J.; Abdulazeez, A.M. (2021). Deep Learning Convolutional Neural Network for Face Recognition: A Review. International Journal of Science and Business, 5, 41–55. http://doi.org/10.5281/zenodo.4471013.

Wahyuni, E. S.; Hendri, M. (2019). Smoke and Fire Detection Base on Convolutional Neural Network. ELKOMIKA Journal of Electrical Engineering, Energy, Telecommunication, Electronics, 7, 455–465. http://doi.org/10.26760/elkomika.v7i3.455.

Dzierżak, R.; Omiotek, Z. (2022). Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis. Sensors, 22, 8189. http://doi.org/10.3390/s22218189.

Wang, Q.; Yu, Z.; Zhang, Y. (2021). Face recognition Using Convolutional Neural Networks. 2nd International Conference on Machine Learning and Computational Applications - ICMLCA 2021, pp. 144–148.

Mishra, B.K.; Thakker, D.; Mazumdar, S.; Neagu, D.; Gheorghe, M.; Simpson, S. (2020). A novel application of deep learning with image cropping: a smart city use case for flood monitoring. Journal of Reliability and Intelligent Environments, 6, 51–61. http://doi.org/10.1007/s40860-020-00099-x.

Shorten, C.; Khoshgoftaar, T.M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6, 60. http://doi.org/10.1186/s40537-019-0197-0.

Hadiyoso, S.; Aulia, S. (2023). Automatic Leukocytes Classification using Deep Convolutional Neural Network, ELKOMIKA Journal of Electrical Engineering, Energy, Telecommunication, Electronics, 11, 195–206. https://doi.org/10.26760/elkomika.v11i1.195.

Mukherjee, S. (2019). Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification. Neurocomputing, 378, 112–119. http://doi.org/10.1016/j.neucom.2019.10.008.

Lv, Q.; Zhang, S.; Wang, Y. (2022). Deep Learning Model of Image Classification Using Machine Learning. Advances in Multimedia, 2022, 3351256. http://doi.org/10.1155/2022/3351256.

Zhang, Y.; Pan, C.; Sun, J.; Tang, C. (2018). Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. Journal of Computer Science, 28, 1‒10. http://doi.org/10.1016/j.jocs.2018.07.003.

Poernomo, A.; Kang, D. (2018). Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network. Neural Networks, 104, 60–67. http://doi.org/10.1016/j.neunet.2018.03.016.

He, C.; Ma, M.; Wang, P. (2020). Extract interpretability-accuracy balanced rules from artificial neural networks: A review. Neurocomputing, 387, 346–358. http://doi.org/10.1016/j.neucom.2020.01.036.

Roslidar, R.; Saddami, K.; Arnia, F.; Syukri, M.; Munadi, K. (2019). A study of fine-tuning CNN models based on thermal imaging for breast cancer classification. 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), pp. 77–81. http://doi.org/10.1109/CYBERNETICSCOM.2019.8875661.

Read Full-Text
About this article

SUBMITTED: 23 June 2023
ACCEPTED: 24 July 2023
PUBLISHED: 8 August 2023
SUBMITTED to ACCEPTED: 31 days
DOI: https://doi.org/10.53623/gisa.v3i2.270

Cite this article
Melinda, M. ., Yunidar, Y. ., & Andryani, N. A. C. . (2023). Application of Convolutional Neural Network (CNN) Method in Fluctuations Pattern. Green Intelligent Systems and Applications, 3(2), 56–68. https://doi.org/10.53623/gisa.v3i2.270
Keywords
fluctuation pattern High High- fluctuation Convolutional Neural Network
Accessed
276
Citations
0
Share this article