Klasifikasi Penyakit Daun Jagung Menggunakan Lightweight Convolutional Neural Network
Abstract
Deep Neural Networks have achieved great success in many tasks and research especially in pattern recognition. However, high computing and large memory requirements prevent them from being applied to limited devices and require large costs. And Imbalance of data in Machine Learning refers to the unequal distribution of classes in a data set. This problem is often encountered in classification tasks where the distribution of classes within a given data set is not uniform. Given these problems, the researchers proposed the Light Neural Network method and added the Resampling method to Corn Leaf Disease data, to ease the computational process and balance the data. Researchers use several methods in machine learning which involve the use of knowledge that has been learned by existing models (Transfer Learning), including: MobileNet, EfficientNetB1 and Exception, as well as conducting comparisons between models. These models use the Depthwise Separable Convolution technique to reduce the number of parameters and convolution operations that need to be performed, thereby reducing the computational requirements and model complexity. The results of light neural network research and the combination of Resampling, the highest Transfer Learning model of 95% obtained from the EfficientNetB1 model than other Transfer learning models.
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Ustyannie, Windyaning, and S. Suprapto. 2020. “Oversampling Method To Handling Imbalanced Datasets Problem in Binary Logistic Regression Algorithm.” IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14(1):1. doi: 10.22146/ijccs.37415.
Azizah, Qudsiah Nur. 2023. “Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network AlexNet.” Sudo Jurnal Teknik Informatika 2(1):28–33. doi: 10.56211/sudo.v2i1.227.
Bock, Sebastian, and Martin Weis. 2019. “A Proof of Local Convergence for the Adam Optimizer.” Proceedings of the International Joint Conference on Neural Networks 2019-July(July):1–8. doi: 10.1109/IJCNN.2019.8852239.
Danon, Dov, Moab Arar, Daniel Cohen-Or, and Ariel Shamir. 2021. “Image Resizing by Reconstruction from Deep Features.” Computational Visual Media 7(4):453–66. doi: 10.1007/s41095-021-0216-x.
Dollár, Piotr, Mannat Singh, and Ross Girshick. 2021. “Fast and Accurate Model Scaling.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 924–32. doi: 10.1109/CVPR46437.2021.00098.
Dong, Shi, Ping Wang, and Khushnood Abbas. 2021. “A Survey on Deep Learning and Its Applications.” Computer Science Review 40:100379. doi: 10.1016/j.cosrev.2021.100379.
Dr.A, .Usha Ruby. 2020. “Binary Cross Entropy with Deep Learning Technique for Image Classification.” International Journal of Advanced Trends in Computer Science and Engineering 9(4):5393–97. doi: 10.30534/ijatcse/2020/175942020.
Gu, Minghong, Yuejun Zhang, Yongzhong Wen, Guangpeng Ai, Huihong Zhang, Pengjun Wang, and Guoqing Wang. 2023. “A Lightweight Convolutional Neural Network Hardware Implementation for Wearable Heart Rate Anomaly Detection.” Computers in Biology and Medicine 155:106623. doi: https://doi.org/10.1016/j.compbiomed.2023.106623.
Hasnain, Muhammad, Muhammad Fermi Pasha, Imran Ghani, Muhammad Imran, Mohammed Y. Alzahrani, and Rahmat Budiarto. 2020. “Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking.” IEEE Access 8:90847–61. doi: 10.1109/ACCESS.2020.2994222.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. “Resampling Methods.” Pp. 175–201 in An Introduction to Statistical Learning: with Applications in R. New York, NY: Springer New York.
Khushi, Matloob, Kamran Shaukat, Talha Mahboob Alam, Ibrahim A. Hameed, Shahadat Uddin, Suhuai Luo, Xiaoyan Yang, and Maranatha Consuelo Reyes. 2021. “A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data.” IEEE Access 9:109960–75. doi: 10.1109/ACCESS.2021.3102399.
Lin, Min, Qiang Chen, and Shuicheng Yan. 2014. “Network in Network.” 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings 1–10.
Liu, Bingyan, Yifeng Cai, Yao Guo, and Xiangqun Chen. 2021. “TransTailor: Pruning the Pre-Trained Model for Improved Transfer Learning.” 35th AAAI Conference on Artificial Intelligence, AAAI 2021 10A:8627–34. doi: 10.1609/aaai.v35i10.17046.
Liu, Jia, Quan Zhou, Yong Qiang, Bin Kang, Xiaofu Wu, and Baoyu Zheng. 2020. “FDDWNET : A LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORK FOR REAL-TIME SEMANTIC SEGMENTATION National Engineering Research Center of Communications and Networking , School of Internet of Things , Nanjing University of Posts & Telecommunications , P . R . Chin.” ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2373–77.
Niu, Zhaoyang, Guoqiang Zhong, and Hui Yu. 2021. “A Review on the Attention Mechanism of Deep Learning.” Neurocomputing 452:48–62. doi: 10.1016/j.neucom.2021.03.091.
Peng, Han, Weikang Gong, Christian F. Beckmann, Andrea Vedaldi, and Stephen M. Smith. 2021. “Accurate Brain Age Prediction with Lightweight Deep Neural Networks.” Medical Image Analysis 68:101871. doi: 10.1016/j.media.2020.101871.
Rianto P, and Harjoko A. 2017. “Penentuan Kematangan Buah Salak Pondoh Di Pohon Berbasis Pengolahan Citra Digital.” IJCCS : Indonesian Journal of Computing and Cybernetics Systems 11(2):143–54.
Rochman Fadlur, Junaedi Hartarto. 2020. “No Title.” IMPLEMENTASI TRANSFER LEARNING UNTUK IDENTIFIKASI ORDO TUMBUHAN MELALUI DAUN Vol. 1 No.
Rosadi, Muhammad Imron, Moch Lutfi, and Sejarah Artikel. n.d. “Identifikasi Jenis Penyakit Daun Jagung Menggunakan Deep Learning Pre-Trained Model INFO ARTIKEL ABSTRAK.” doi: 10.35891/explorit.
Sarkar, Dipanjan, Raghav Bali, and Tamoghna Ghosh. 2018. Hands-On Transfer Learning with Python: Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras. Packt Publishing Ltd.
Shin, Kyoham, Jongmin Han, and Seokho Kang. 2021. “MI-MOTE: Multiple Imputation-Based Minority Oversampling Technique for Imbalanced and Incomplete Data Classification.” Information Sciences 575:80–89. doi: https://doi.org/10.1016/j.ins.2021.06.043.
Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N. PlantDoc. 2021. “Corn or Maize Leaf Disease Dataset.” Retrieved (https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset).
Spectogram, Mel, and Transfer Learning. 2022. “INTISARI Audio-Visual Convolutional Neural Networks Menggunakan Transfer Learning Untuk Deteksi Iklan Pada Video Siaran TV Audio-Visual Convolutional Neural Networks Using Transfer Learning for Commercial Break Detection in Video Broadcasting TV.” 2–3.
Sulistiyono, Mulia, Yoga Pristyanto, Sumarni Adi, and Gagah Gumelar. 2021. “Implementasi Algoritma Synthetic Minority Over-Sampling Technique Untuk Menangani Ketidakseimbangan Kelas Pada Dataset Klasifikasi.” Sistemasi 10(2):445. doi: 10.32520/stmsi.v10i2.1303.
Tugrul, Bulent, Elhoucine Elfatimi, and Recep Eryigit. 2022. “Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review.” Agriculture 12(8):1192. doi: 10.3390/agriculture12081192.
Ustyannie, Windyaning, and S. Suprapto. 2020. “Oversampling Method To Handling Imbalanced Datasets Problem in Binary Logistic Regression Algorithm.” IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14(1):1. doi: 10.22146/ijccs.37415.
Published
2023-07-31
How to Cite
Hermawan, A. (2023). Klasifikasi Penyakit Daun Jagung Menggunakan Lightweight Convolutional Neural Network. JIIFKOM (Jurnal Ilmiah Informatika Dan Komputer), 2(2), 1 - 7. https://doi.org/https://doi.org/10.51901/jiifkom.v2i2.347
Section
Articles
