Penerapan Neural Network Dalam Klasifikasi Citra Permainan Batu Kertas Gunting dengan Probabilistic Neural Network
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Siti Julianita Siregar
*
STMIK Triguna Dharma, Medan,
Indonesia
- Ahmadi Irmansyah Lubis STMIK Triguna Dharma, Medan, Indonesia
- Erika Fahmi Ginting STMIK Triguna Dharma, Medan, Indonesia (*) Corresponding Author
Abstract
In this research, an image classification model was developed to distinguish hand objects pointing at rock, paper, and scissors using one of the popular image classification methods, namely the Probabilistic Neural Network. Probabilistic Neural Network is a method in an artificial neural network that is used to classify a category based on the results of calculating the distance between the density function and the probability. PNN has 4 stages of processing, namely Input Layer, Pattern Layer, Summation Layer, and Output Layer. Tests in the study were carried out with a total of 60 testing data from three object classes from the dataset. Then the results of the classification of Batu, Scissors, and Paper hand images using the application of the PNN algorithm in this research test obtained an average accuracy value of 90%
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References
M. F. Naufal et al., “Klasifikasi Citra Game Batu Kertas Gunting Menggunakan Convolutional Neural Network,” Techno.Com, vol. 20, no. 1, pp. 166–174, 2021, doi: 10.33633/tc.v20i1.4273.
M. D. Wuryandari and I. Afrianto, “Perbandingan Metode Jaringan Syaraf Tiruan Backpropagation Dan Learning Vector Quantization Pada Pengenalan Wajah,” Komputa, vol. 1, no. 1, pp. 45–51, 2012.
Y. Zeinali and B. A. Story, “Competitive probabilistic neural network,” Integrated Computer-Aided Engineering, vol. 24, no. 2, pp. 105–118, 2017, doi: 10.3233/ICA-170540.
J. Permadi and A. Harjoko, “Identifikasi Penyakit Cabai Berdasarkan Gejala Bercak Daun dan Penampakan Conidia Menggunakan Probabilistic Neural Network,” Semnaskit 20152, pp. 49–53, 2015.
F. Alfiyan, “Pengaruh Perbedaan Kernel Edge Detection Kirsch Pada Sketching Citra Digital Dengan Bahasa Pemrograman Matlab,” Jurnal Teknologi Informasi (JTI) RESPATI, vol. XIII, no. November, pp. 1–8, 2017, [Online]. Available: http://jti.respati.ac.id/index.php/jurnaljti/article/view/190.
E. T. Wijaya and I. W. Al Farqi, “Aplikasi Pengenalan Aksara Carakan Madura Dengan Menggunakan Metode Backpropagation,” Jurnal Ilmiah Teknologi Informasi Asia, vol. 9, no. 1, pp. 18–34, 2015.
N. M. Sasi and V. K. Jayasree, “Contrast Limited Adaptive Histogram Equalization for Qualitative Enhancement of Myocardial Perfusion Images,” Engineering, vol. 05, no. 10, pp. 326–331, 2013, doi: 10.4236/eng.2013.510b066.
Y. Chang, C. Jung, P. Ke, H. Song, and J. Hwang, “Automatic Contrast-Limited Adaptive Histogram Equalization with Dual Gamma Correction,” IEEE Access, vol. 6, no. c, pp. 11782–11792, 2018, doi: 10.1109/ACCESS.2018.2797872.
S. A. Hammam, T. W. Purboyo, and R. E. Saputra, “Cotton texture segmentation based on image texture analysis using gray level run length and euclidean distance,” Journal of Theoretical and Applied Information Technology, vol. 95, no. 24, pp. 6915–6923, 2017.
A. Kadir, L. E. Nugroho, A. Susanto, and P. I. Santosa, “Leaf Classification Using Shape, Color, and Texture Features,” pp. 225–230, 2013, [Online]. Available: http://arxiv.org/abs/1401.4447.
F. H. Mahmood and W. A. Abbas, “Texture Features Analysis using Gray Level Co-occurrence Matrix for Abnormality Detection in Chest CT Images,” Abbas Iraqi Journal of Science, vol. 57, no. 1A, pp. 279–288, 2016.
S. Singh, D. Srivastava, and S. Agarwal, “GLCM and its application in pattern recognition,” 5th International Symposium on Computational and Business Intelligence, ISCBI 2017, pp. 20–25, 2017, doi: 10.1109/ISCBI.2017.8053537.
K. S. Ahmad, A. S. Thosar, J. H. Nirmal, and V. S. Pande, “A unique approach in text independent speaker recognition using MFCC feature sets and probabilistic neural network,” ICAPR 2015 - 2015 8th International Conference on Advances in Pattern Recognition, 2015, doi: 10.1109/ICAPR.2015.7050669.
A. T. Azar and S. A. El-Said, “Probabilistic neural network for breast cancer classification,” Neural Computing and Applications, vol. 23, no. 6, pp. 1737–1751, 2013, doi: 10.1007/s00521-012-1134-8.
M. Kusy and P. A. Kowalski, “Weighted probabilistic neural network,” Information Sciences, vol. 430–431, pp. 65–76, 2018, doi: 10.1016/j.ins.2017.11.036.
M. N. Mohanty and H. K. Palo, “Child emotion recognition using probabilistic neural network with effective features,” Measurement: Journal of the International Measurement Confederation, vol. 152, p. 107369, 2020, doi: 10.1016/j.measurement.2019.107369.
R. Chandra, E. B. Nababan, and Sawaluddin, “Identifikasi Penyakit Diabetic Retinopathy menggunakan Learning Vector Quantization ( LVQ ),” InfoTekJar : Jurnal Nasional Informatika dan Jaringan, vol. 1, no. 6, pp. 0–5, 2021.
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Copyright (c) 2021 Siti Julianita Siregar, Ahmadi Irmansyah Lubis, Erika Fahmi Ginting
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