Pengenalan Jamur Yang Dapat Dikonsumsi Menggunakan Metode Transfer Learning Pada Convolutional Neural Network

  • Elok Iedfitra Haksoro Program Studi Sarjana Teknik Informatika, Universitas Dian Nuswantoro, Semarang, Indonesia
  • Abas Setiawan Program Studi Sarjana Teknik Informatika, Universitas Dian Nuswantoro, Semarang, Indonesia
Keywords: convolutional neural network, jamur dapat dikonsumsi, MobileNets, MobileNetV2, transfer learning

Abstract

Not all mushrooms are edible because some are poisonous. The edible or poisonous mushrooms can be identified by paying attention to the morphological characteristics of mushrooms, such as shape, color, and texture. There is an issue: some poisonous mushrooms have morphological features that are very similar to edible mushrooms. It can lead to the misidentification of mushrooms. This work aims to recognize edible or poisonous mushrooms using a Deep Learning approach, typically Convolutional Neural Networks. Because the training process will take a long time, Transfer Learning was applied to accelerate the learning process. Transfer learning uses an existing model as a base model in our neural network by transferring information from the related domain. There are Four base models are used, namely MobileNets, MobileNetV2, ResNet50, and VGG19. Each base model will be subjected to several experimental scenarios, such as setting the different learning rate values for pre-training and fine-tuning. The results show that the Convolutional Neural Network with transfer learning method can recognize edible or poisonous mushrooms with more than 86% accuracy. Moreover, the best accuracy result is 92.19% obtained from the base model of MobileNetsV2 with a learning rate of 0,00001 at the pre-training stage and 0,0001 at the fine-tuning stage.

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References

I. Annissa, Ekamawanti, H. Artuti, and Wahdina, “Keanekaragaman Jenis Jamur Makrokopis Di Arboretum Sylva Universitas Tanjungpura,” J. Hutan Lestari, vol. 5, no. 4, pp. 969–977, 2017.
W. Darwis, Desnalianif, and R. Supriati, “Inventarisasi Jamur Yang Dapat Dikonsumsi Dan Beracun Yang Terdapat Di Hutan Dan Sekitar Desa Tanjung Kemuning Kaur Bengkulu,” J. Ilm. Konserv. Hayati, vol. 07, no. 02, pp. 1–8, 2011.
A. Zubair and A. R. Muslikh, “Identifikasi jamur menggunakan metode k-nearest neighbor dengan ekstraksi ciri morfologi,” Semin. Nas. Sist. Inf., no. September, pp. 965–972, 2017.
S. A. Prayoga, I. Nawangsih, and T. N. Wiyatno, “Implementasi Metode Naïve Bayes Classifier Untuk Identifikasi Jenis Jamur,” vol. 14, no. 1, pp. 55–66, 2019.
I. W. S. E. P, A. Y. Wijaya, and R. Soelaiman, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, 2016.
H. Abhirawan, Jondri, and A. Arifianto, “Pengenalan Wajah Menggunakan Convolutional Neural Networks (CNN),” Univ. Telkom, vol. 4, no. 3, pp. 4907–4916, 2017.
A. Gholamy, V. Kreinovich, and O. Kosheleva, “Why 70/30 or 80/20 Relation Between Training and Testing Sets : A Pedagogical Explanation,” Dep. Tech. Reports, pp. 1–6, 2018.
Y. Yuhandri, “Perbandingan Metode Cropping Pada Sebuah Citra Untuk Pengambilan Motif Tertentu Pada Kain Songket Sumatera Barat,” Komtekinfo, vol. 6, no. 1, pp. 95–105, 2019.
S. C. Wong, A. Gatt, V. Stamatescu, and M. D. McDonnell, “Understanding Data Augmentation for Classification: When to Warp?,” 2016 Int. Conf. Digit. Image Comput. Tech. Appl. DICTA 2016, 2016.
K. Weiss, T. M. Khoshgoftaar, and D. D. Wang, A survey of transfer learning, vol. 3, no. 1. Springer International Publishing, 2016.
M. Mateen, J. Wen, Nasrullah, S. Song, and Z. Huang, “Fundus image classification using VGG-19 architecture with PCA and SVD,” Symmetry (Basel)., vol. 11, no. 1, 2019.
E. Rezende, G. Ruppert, T. Carvalho, F. Ramos, and P. De Geus, “Malicious software classification using transfer learning of ResNet-50 deep neural network,” Proc. - 16th IEEE Int. Conf. Mach. Learn. Appl. ICMLA 2017, vol. 2017-Decem, no. January 2018, pp. 1011–1014, 2017.
A. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv, 2017.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 4510–4520, 2018.
F. Chollet, “Transfer learning and chess,” TensorFlow, 2017. [Online]. Available: https://www.tensorflow.org/tutorials/images/transfer_learning#configure_the_dataset_for_performance. [Accessed: 03-Nov-2020].
S. Sena, “Pengenalan Deep Learning Part 3 : BackPropagation Algorithm,” Medium, 2017. [Online]. Available: https://medium.com/@samuelsena/pengenalan-deep-learning-part-3-backpropagation-algorithm-720be9a5fbb8.
Published
2021-09-10
Section
Articles
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