Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods

Authors

  • Rizka Zulfani Syahrir Student
  • Eri Prasetyo Wibowo Universitas Gunadarma

DOI:

https://doi.org/10.34306/itsdi.v3i1.491

Keywords:

Leaves, Supervised Learning, Convolutional Neural Network, Classification, Accuracy

Abstract

One part of the tree, namely the leaves, which grow on the branches, has several types of leaves consisting of 4 shapes, ranging from circular shapes, elongated shapes, and some even have a finger shape. Often we mistake the shapes of these leaves. This study discusses the classification of leaves based the shape of the leaf bones using the Convolutional Neural Network, which is used to classify data that has been labeled using one of the methods, namely supervised learning. The purpose of this method is to classify a variable into the variables that have been listed. The goal is to classify leaves based on leaf shape to implement a Convolutional Neural Network algorithm model for leaf classification based on bone shape, which will produce an accuracy value. Accuracy values are obtained from conducting experiments at the training and trial stages. So it can be concluded using the epochs parameter of 30 and a batch size of 128, using ReLU and Softmax activations. The results obtained for the accuracy value for training are 98.52%, while the validation is 89.06%.

Downloads

Download data is not yet available.

References

Naglot, D., Kasliwal, P. S., Gaikwad, S. J., & Agrawal, N. D. (2019). Indian Plant Recognition System Using Convolutional Neural Network. International Journal Of Computer Sciences and Engineering, 7(6), 276-280. doi:https://doi.org/10.26438/ijcse/v7i6.276280

Wisudawati, L. M., Madenda, S., Wibowo, E. P., & Abdullah, A. A. (2020). Feature Extraction Optimization with Combination 2D-Discrete Wavelet Transform and Gray Level Co-Occurrence Matrix for Classifying Normal and Abnormal Breast Tumors. Modern Applied Science, 14, 51-62

Manis, S. (2019). Pengertian Daun : Fungsi, Struktur Bagian dan Jenis Daun Pada Tumbuhan. Diambil kembali dari Pelajaran : https://www.pelajaran.co.id/2019/03/daun.html

Aulia, K. (2017). Bentuk-bentuk Tulang Daun pada Tumbuhan Hijau. Diambil kembali dari Juragan Les: https://www.juraganles.com/2017/08/bentuk-bentuk-tulang-daun-pada-tumbuhan-hijau.html

Guo, Y., & al, e. (2015). Deep learning for visual understanding: A review. Neurocomputing, 1-22. doi:https://doi.org/10.1016/j.neucom.2015.09.116

Goodfellow, I., Bengio, Y., & Aaron, C. (2016). Deep Learning. MIT Press.

Wehle, H.-D. (2017). Machine Learning, Deep Learning and AI: What’s the Difference ? Data Scientist Innovation Day, (hal. 2-5)

Zheng, Y., Gao, Z., Wang, Y., & Fu, Q. (2020). MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series. 225324-225335.

Syulistyo, A. R., Purnomo, D. M., Rachmadi, M. F., & Wibowo, A. (2016). Particle Swarm Optimization (PSO) For Training Optimization On Convolutional Neural Network (CNN). Journal of Computer Science and Information, 9(1), 52-58. doi:https://doi.org/10.21609/jiki.v9i1.366

Lina, Q. (2019). Apa itu Convolutional Neural Network? Diambil kembali dari Medium: https://medium.com/@16611110/apa-itu-convolutional-neural-network-836f70b193a4

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929-1958.

Putra, I. W. (2016). Image Classification Using Convolution Neural Network (CNN) on Caltech 101. Undergraduate Thesis, Institut Teknologi Sepuluh Nopember, Surabaya.

Agarap, A. F. (2019). Deep Learning using Rectified Linear Units (ReLU). doi:arXiv:1803.08375

Ilahiyah, S., & Nilogiri, A. (2018). Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network. JUSTINDO(Jurnal Sistem & Teknologi Informasi Indonesia), 3(2), 49-56. doi:https://doi.org/10.32528/justindo.v3i2.2254

Team. (2020, December 26). Optimizers. Diambil kembali dari ML Glossary: https://ml-cheatsheet.readthedocs.io/_/downloads/en/latest/pdf/

Categorical Crossentropy. (t.thn.). Diambil kembali dari Peltarion: https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/loss-functions/categorical-crossentropy

Santra, A., & Christy, C. J. (2012). Genetic Algorithm and Confusion Matrix for Document Clustering. IJCSI International Journal of Computer Science Issues, 322-328.

Lutz, M. (2010). Programming Python, Fourth Edition. California: O’Reilly Media, Inc.

Fanghor, H. (2015). Python for Computational Science and Engineering. Zenodo.

Supardi, Y. (2017). Semua Bisa Menjadi Programmer Python Basic. Jakarta: PT Elex Media Komputindo.

Pangestu, M. A., & Bunyamin, H. (2018). Analisis Performa dan Pengembangan Sistem Deteksi Ras Anjing pada Gambar dengan Menggunakan Pre-Trained CNN Model. Jurnal Teknik Informatika dan Sistem Informasi, 4(2), 337-344.

Harahap, R.A., Wibowo, E.P. and Harahap, R.K. (2020). Detection and Simulation of Vacant Parking Lot Space Using EAST Algorithm and Haar Cascade. 2020 Fifth International Conference on Informatics and Computing (ICIC), Gorontalo, 2020.

Aziz, M., & Aman, M. (2019). Decision Support System For Selection Of Expertise Using Analytical Hierarchy Process Method. IAIC Transactions on Sustainable Digital Innovation, 1(1), 49-65.

Rahardja, U., Andayani, D., Aristo, N. C., & Hasibuan, Z. A. (2019). Application Of Trial Finalization System As Determinants Of Final Thesis Session Results. IAIC Transactions on Sustainable Digital Innovation, 1(1), 1-7.

Downloads

Published

2021-10-31

How to Cite

Syahrir, R. Z., & Eri Prasetyo Wibowo. (2021). Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 3(1), 1–7. https://doi.org/10.34306/itsdi.v3i1.491

Issue

Section

Articles