Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image

https://doi.org/10.22146/ijccs.42036

Muhammad Faqih Dzulqarnain(1*), Suprapto Suprapto(2), Faizal Makhrus(3)

(1) Master Program in Computer Science, FMIPA UGM, Yogyakarta, Indonesia
(2) Departement of Computer Science and Electronics, FMIPA UGM, Yogyakarta, Indonesia
(3) Departement of Computer Science and Electronics, FMIPA UGM, Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


Salak is a seasonal fruit that has high export value. The success of salak fruit exported is influence by selection process, but there is still a problem in it. The selection of salak still done manually and potentially misclassified. Research to automate the selection of salak fruit has been done before. The process of selection this salak fruits used convolutional neural network (CNN) based on image of salak fruits. The resulting of accuracy value from previous research is 70.7% for four class classification model and 81.45% for two class classification model. This research was conducted to increase accuracy value the classification of salak exported based on previous research. Accuracy improvement by changing the noise removal process to produce a better image. The changing also occur in the CNN architecture that layer convolution is more deep and with additional parameters such as Stride, Zero Padding, and Adam Optimizer. This change hopefully can increase the accuracy value of the salak classification. The results showed an accuracy value increased 22.72% from 70.70% to 93.42% for the category of four classes CNN models and increased 13,29% from 81.45% to 94.74% for category two classes.

Keywords


sorting salak fruit; Convolutional Neural Network; digital image; increased accuracy; parameter

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References

[1]          Rismiyati and SN. Azhari, “Convolutional Neural Network implementation for image-based Salak sortation,” ICST (International Conference on Science and Technology-Computer, 27-28 Oct. 2016 [Online]. Available:https://ieeexplore.ieee.org/document/7877351. [Accessed: 28-Aug-2018]

[2]          P. Rianto and A. Harjoko, “Penentuan Kematangan Buah Salak Pondoh di Pohon Berbasis Pengolahan Citra Digital” IJCCS (Indonesian J. Comput. Cybern. Syst., vol.11, no. 2, 2017 [Online]. Available:https://jurnal.ugm.ac.id/ijccs/article/view/17416. [Accessed: 10-Dec-2018]

[3]          R. C. Gonzalezand, and R. E. Woods, Digital Image Processing, Ed.3, New Jersey: Prentice Hall, 2008.

[4]          N. Otsu, “A Threshold Selection Method from Gray-level Histogram,” IEEE Transactions on System, Man, and Cybernetics, vol.9, no.1, 1979 [Online]. Available: https://ieeexplore.ieee.org/document/4310076. [Accessed: 28-Aug-2018]

[5]          C.S. Nandi, B. Tudu. and C. Koley, “A Machine Vision-Based Maturity Prediction System for Sorting of Harvested Mangoes,” IEEE Transactions on Instrumentation and Measurement., vol.63, no.7, 2014 [Online]. Available:https://ieeexplore.ieee.org/document/6730653. [Accessed: 28-Aug-2018]

[6]          S.R. Rupanagudi, G.B. Varsa, dan B.S. Ranjani, “A cost effective tomato maturity grading system using image processing for farmers,” IC3I (International Conference on Contemporary Computing and Informatics, 27-29 Nov. 2014 [Online]. Available:https://ieeexplore.ieee.org/document/7019591. [Accessed: 28-Aug-2018]

[7]          B. Xin, T. Wang, and T. Tang, “A deep learning and softmax regression fault diagnosis method for multi-level converter,” SDEMPED (International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, 29 Aug.-1 Sept. 2017 [Online]. Available:https://ieeexplore.ieee.org/document/8062370. [Accessed: 5-Sep-2018]

[8]          Y. Luo, H. Cheng, and L. Yang, “Size-Invariant Fully Convolutional Neural Network for vessel segmentation of digital retinal images,” APSIPA (Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 13-16 Dec. 2016 [Online]. Available:https://ieeexplore.ieee.org/document/7820677. [Accessed: 28-Aug-2018]

[9]          Y. Pang, M. Sun, X. Jiang, and X. Li, “Convolution in Convolution for Network in Network,” IEEE Transactions on Neural Networks and Learning Systems, vol.29, no. 5, 2018 [Online]. Available:https://ieeexplore.ieee.org/document/7879808. [Accessed: 28-Aug-2018]

[10]      D. P. Kingma, and J. Ba, “Adam: A Method for Stochastic Optimization,” Cornell University Library, Jan. 2017 [Online]. Available:https://arxiv.org/abs/1412.6980 [Accessed: 28-Aug-2018]



DOI: https://doi.org/10.22146/ijccs.42036

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