OPTIMASI HYPERPARAMETER CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT TANAMAN PADI
Kata Kunci:
Optimasi Hyperparameter, Convolutional Neural Network, MobileNet-V2, Penyakit Tanaman Padi
Abstrak
Penyakit tanaman merupakan sebuah tantangan pada sektor pertanian terutama bagi petani padi. Mengidentifikasi penyakit pada daun padi merupakan langkah awal untuk memberantas dan mengobati penyakit, sehingga dapat meminimalisir terjadinya gagal panen. Dengan perkembangan cepat dari convolutional neural network (CNN), penyakit daun padi dapat dikenali dengan baik tanpa bantuan seorang ahli. Arsitektur MobileNet-V2 digunakan untuk mengklasifikasi penyakit daun padi karena yang memiliki ukuran yang kecil namun dengan peforma yang baik. Untuk meningkatkan peforma dari model CNN, akan dilakukan optimasi hyperparameter yang terdiri epoch, batch size, learning rate dan optimizer. Penelitian ini bertujuan untuk untuk mendapatkan hyperparameter yang optimal sehingga memberikan peforma yang baik pada model CNN. Dataset yang digunakan terdiri dari 3 kelas penyakit yang menyerang daun tanaman padi antara lain blast, blight dan tungro. Berdasarkan percobaan yang sudah dilakukan, penentuan hyperparameter sangat berpengaruh terhadap peforma model. Hyperparameter dengan jumlah epoch 100, batch size 32, learning rate 0,001 dan optimizer RMSProp memberikan hasil yang paling optimal dengan nilai accuracy 97,56%, precission 97,64%, recall 97,57% dan f1-score 97,57%.
Referensi
[1] B. P. Statistik, Luas Panen dan Produksi Padi Di Indonesia 2021. Indonesia: Badan Pusat Statistik, 2021.
[2] F. Martinelli et al., “Advanced methods of plant disease detection. A review,” Agron. Sustain. Dev., vol. 35, no. 1, pp. 1–25, 2015, doi: 10.1007/s13593-014-0246-1.
[3] M. Turkoglu and D. Hanbay, “Apricot Disease Identification based on Attributes Obtained from Deep Learning Algorithms,” 2018 Int. Conf. Artif. Intell. Data Process. IDAP 2018, no. September 2018, 2019, doi: 10.1109/IDAP.2018.8620831.
[4] J. G. A. Barbedo, “Digital Image Processing for Detecting and Classifying Plant Diseases,” Springerplus, vol. 2, 2013, doi: 10.22632/ccs-2017-252-66.
[5] F. Jiang, Y. Lu, Y. Chen, D. Cai, and G. Li, “Image recognition of four rice leaf diseases based on deep learning and support vector machine,” Comput. Electron. Agric., vol. 179, no. October, p. 105824, 2020, doi: 10.1016/j.compag.2020.105824.
[6] R. P. Ramadhan and N. L. Marpaung, “Identifikasi jenis penyakit daun tanaman jagung menggunakan jaringan saraf tiruan berbasis backpropagation,” Jom FTEKNIK, vol. 6, no. 1, pp. 1–5, 2019.
[7] S. Kumar et al., “Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19,” Comput. Electr. Eng., vol. 103, p. 108391, 2022, doi: 10.1016/j.compeleceng.2022.108391.
[8] N. F. F. Alshdaifat, A. Z. Talib, and M. A. Osman, “Improved deep learning framework for fish segmentation in underwater videos,” Ecol. Inform., vol. 59, 2020, doi: 10.1016/j.ecoinf.2020.101121.
[9] Y. Yuan, L. Chen, H. Wu, and L. Li, “Advanced agricultural disease image recognition technologies: A review,” Inf. Process. Agric., pp. 1–12, 2021, doi: 10.1016/j.inpa.2021.01.003.
[10] Y. LeCun et al., “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Comput., vol. 1, no. 4, pp. 541–551, 1989, doi: 10.1162/neco.1989.1.4.541.
[11] J. W. G. Putra, Pengenalan Konsep Pembelajaran Mesin dan Deep Learning, vol. 4. 2020.
[12] Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, “Identification of rice diseases using deep convolutional neural networks,” Neurocomputing, vol. 267, pp. 378–384, Dec. 2017, doi: 10.1016/j.neucom.2017.06.023.
[13] S. Ghosal and K. Sarkar, “Rice Leaf Diseases Classification Using CNN With Transfer Learning,” in 2020 IEEE Calcutta Conference, Feb. 2020, pp. 230–236, doi: 10.1109/CALCON49167.2020.9106423.
[14] R. R. and D. Park, “A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no.1, pp. 67–70, 2018, doi: 10.14569/IJACSA.2018.090109.
[15] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput. Electron. Agric., vol. 173, no. November 2019, p. 105393, Jun. 2020, doi: 10.1016/j.compag.2020.105393.
[16] A. Julianto and A. Sunyoto, “A performance evaluation of convolutional neural network architecture for classification of rice leaf disease,” IAES Int. J. Artif. Intell., vol. 10, no. 4, pp. 1069–1078, 2021, doi: 10.11591/IJAI.V10.I4.PP1069-1078.
[17] R. Riswandi, R. Jamiah, N. Mardhatillah, and H. P. Hamid, “Klasifikasi Penyakit Pada Citra Daun Jeruk Menggunakan Arsitektur MobileNet berbasis Mobile Platform,” J. Fokus Elektroda Energi List. Telekomun. Komputer, Elektron. dan Kendali), vol. 6, no. 4, p. 212, 2021, doi: 10.33772/jfe.v6i4.19113.
[18] Putri Teresia Ompusunggu;, “Klasifikasi Penyakit Tanaman Pada Daun Kentang Dengan Metode Convolutional Neural Network Arsitektur Mobilenet,” J. Syntax Fusion, vol. 2, no. 9, pp. 740–751, 2022, doi: 10.54543/fusion.v2i09.217.
[19] F. Hutter, L. Kotthoff, and J. Vanschoren, Automated machine learning: methods, systems, challenges. Springer Nature, 2019.
[20] P. Lacerda, B. Barros, C. Albuquerque, and A. Conci, “Hyperparameter optimization for COVID-19 pneumonia diagnosis based on chest CT,” Sensors, vol. 21, no. 6, pp. 1–11, 2021, doi: 10.3390/s21062174.
[21] A. E. Minarno, M. Hazmi Cokro Mandiri, Y. Munarko, and H. Hariyady, “Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, 2021, doi: 10.22219/kinetik.v6i2.1219.
[22] I. G. T. Isa and B. Junedi, “Hyperparameter Tuning Epoch Dalam Meningkatkan Akurasi Data Latih Dan Data Validasi Pada Citra Pengendara,” Pros. Semin. Nas. Sains dan Teknol., vol. 12, no. 1, pp. 231–237, 2022.
[23] Y. N. Fuadah, I. D. Ubaidullah, N. Ibrahim, F. F. Taliningsing, N. K. Sy, and M. A. Pramuditho, “Optimasi Convolutional Neural Network dan K-Fold Cross Validation pada Sistem Klasifikasi Glaukoma,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 3, p. 728, 2022, doi: 10.26760/elkomika.v10i3.728.
[24] N. Rochmawati, H. B. Hidayati, Y. Yamasari, H. P. A. Tjahyaningtijas, W. Yustanti, and A. Prihanto, “Analisa Learning Rate dan Batch Size pada Klasifikasi Covid Menggunakan Deep Learning dengan Optimizer Adam,” J. Inf. Eng. Educ. Technol., vol. 5, no. 2, pp. 44–48, 2021, doi: 10.26740/jieet.v5n2.p44-48.
[25] D. Motta et al., “Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes,” PLoS One, vol. 15, no. 7, pp. 1–30, 2020, doi: 10.1371/journal.pone.0234959.
[26] S. Bhattacharya, A. Mukherjee, and S. Phadikar, “A Deep Learning Approach for the Classification of Rice Leaf Diseases,” Adv. Intell. Syst. Comput., vol. 1109, no. May 2020, pp. 61–69, 2020, doi: 10.1007/978-981-15-2021-1_8.
[27] A. A. J. V. Priyangka and I. M. S. Kumara, “Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method,” Lontar Komput. J. Ilm. Teknol. Inf., vol. 12, no. 2, p. 123, 2021, doi: 10.24843/lkjiti.2021.v12.i02.p06.
[28] W. Widystuti and J. B. B. Darmawan, “Pengaruh jumlah data set terhadap akurasi pengenalan dalam deep convolutional network,” Konf. Nas. Sist. Inf., pp. 8–9, 2018.
[29] 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, doi: 10.1109/CVPR.2018.00474.
[30] C. Manliguez, “Generalized Confusion Matrix for Multiple Classes,” no. November, pp. 5–7, 2016, doi: 10.13140/RG.2.2.31150.51523.
[31] D. Zhang, J. Wang, and X. Zhao, “Estimating the Uncertainty of Average F1 Scores,” in Proceedings of the 2015 International Conference on The Theory of Information Retrieval, Sep. 2015, pp. 317–320, doi: 10.1145/2808194.2809488.
[32] Faiz Nashrullah, Suryo Adhi Wibowo, and Gelar Budiman, “The Investigation of Epoch Parameters in ResNet-50 Architecture for Pornographic Classification,” J. Comput. Electron. Telecommun., vol. 1, no. 1, pp. 1–8, 2020, doi: 10.52435/complete.v1i1.51.
[33] S. N. Rahmawati, E. W. Hidayat, and H. Mubarok, “Implementasi Deep Learning Pada Pengenalan Aksara Sunda Menggunakan Metode Convolutional Neural Network,” Inser. Inf. Syst. Emerg. Technol. J., vol. 2, no. 1, p. 46, 2021, doi: 10.23887/insert.v2i1.37405.
[2] F. Martinelli et al., “Advanced methods of plant disease detection. A review,” Agron. Sustain. Dev., vol. 35, no. 1, pp. 1–25, 2015, doi: 10.1007/s13593-014-0246-1.
[3] M. Turkoglu and D. Hanbay, “Apricot Disease Identification based on Attributes Obtained from Deep Learning Algorithms,” 2018 Int. Conf. Artif. Intell. Data Process. IDAP 2018, no. September 2018, 2019, doi: 10.1109/IDAP.2018.8620831.
[4] J. G. A. Barbedo, “Digital Image Processing for Detecting and Classifying Plant Diseases,” Springerplus, vol. 2, 2013, doi: 10.22632/ccs-2017-252-66.
[5] F. Jiang, Y. Lu, Y. Chen, D. Cai, and G. Li, “Image recognition of four rice leaf diseases based on deep learning and support vector machine,” Comput. Electron. Agric., vol. 179, no. October, p. 105824, 2020, doi: 10.1016/j.compag.2020.105824.
[6] R. P. Ramadhan and N. L. Marpaung, “Identifikasi jenis penyakit daun tanaman jagung menggunakan jaringan saraf tiruan berbasis backpropagation,” Jom FTEKNIK, vol. 6, no. 1, pp. 1–5, 2019.
[7] S. Kumar et al., “Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19,” Comput. Electr. Eng., vol. 103, p. 108391, 2022, doi: 10.1016/j.compeleceng.2022.108391.
[8] N. F. F. Alshdaifat, A. Z. Talib, and M. A. Osman, “Improved deep learning framework for fish segmentation in underwater videos,” Ecol. Inform., vol. 59, 2020, doi: 10.1016/j.ecoinf.2020.101121.
[9] Y. Yuan, L. Chen, H. Wu, and L. Li, “Advanced agricultural disease image recognition technologies: A review,” Inf. Process. Agric., pp. 1–12, 2021, doi: 10.1016/j.inpa.2021.01.003.
[10] Y. LeCun et al., “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Comput., vol. 1, no. 4, pp. 541–551, 1989, doi: 10.1162/neco.1989.1.4.541.
[11] J. W. G. Putra, Pengenalan Konsep Pembelajaran Mesin dan Deep Learning, vol. 4. 2020.
[12] Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, “Identification of rice diseases using deep convolutional neural networks,” Neurocomputing, vol. 267, pp. 378–384, Dec. 2017, doi: 10.1016/j.neucom.2017.06.023.
[13] S. Ghosal and K. Sarkar, “Rice Leaf Diseases Classification Using CNN With Transfer Learning,” in 2020 IEEE Calcutta Conference, Feb. 2020, pp. 230–236, doi: 10.1109/CALCON49167.2020.9106423.
[14] R. R. and D. Park, “A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no.1, pp. 67–70, 2018, doi: 10.14569/IJACSA.2018.090109.
[15] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput. Electron. Agric., vol. 173, no. November 2019, p. 105393, Jun. 2020, doi: 10.1016/j.compag.2020.105393.
[16] A. Julianto and A. Sunyoto, “A performance evaluation of convolutional neural network architecture for classification of rice leaf disease,” IAES Int. J. Artif. Intell., vol. 10, no. 4, pp. 1069–1078, 2021, doi: 10.11591/IJAI.V10.I4.PP1069-1078.
[17] R. Riswandi, R. Jamiah, N. Mardhatillah, and H. P. Hamid, “Klasifikasi Penyakit Pada Citra Daun Jeruk Menggunakan Arsitektur MobileNet berbasis Mobile Platform,” J. Fokus Elektroda Energi List. Telekomun. Komputer, Elektron. dan Kendali), vol. 6, no. 4, p. 212, 2021, doi: 10.33772/jfe.v6i4.19113.
[18] Putri Teresia Ompusunggu;, “Klasifikasi Penyakit Tanaman Pada Daun Kentang Dengan Metode Convolutional Neural Network Arsitektur Mobilenet,” J. Syntax Fusion, vol. 2, no. 9, pp. 740–751, 2022, doi: 10.54543/fusion.v2i09.217.
[19] F. Hutter, L. Kotthoff, and J. Vanschoren, Automated machine learning: methods, systems, challenges. Springer Nature, 2019.
[20] P. Lacerda, B. Barros, C. Albuquerque, and A. Conci, “Hyperparameter optimization for COVID-19 pneumonia diagnosis based on chest CT,” Sensors, vol. 21, no. 6, pp. 1–11, 2021, doi: 10.3390/s21062174.
[21] A. E. Minarno, M. Hazmi Cokro Mandiri, Y. Munarko, and H. Hariyady, “Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, 2021, doi: 10.22219/kinetik.v6i2.1219.
[22] I. G. T. Isa and B. Junedi, “Hyperparameter Tuning Epoch Dalam Meningkatkan Akurasi Data Latih Dan Data Validasi Pada Citra Pengendara,” Pros. Semin. Nas. Sains dan Teknol., vol. 12, no. 1, pp. 231–237, 2022.
[23] Y. N. Fuadah, I. D. Ubaidullah, N. Ibrahim, F. F. Taliningsing, N. K. Sy, and M. A. Pramuditho, “Optimasi Convolutional Neural Network dan K-Fold Cross Validation pada Sistem Klasifikasi Glaukoma,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 3, p. 728, 2022, doi: 10.26760/elkomika.v10i3.728.
[24] N. Rochmawati, H. B. Hidayati, Y. Yamasari, H. P. A. Tjahyaningtijas, W. Yustanti, and A. Prihanto, “Analisa Learning Rate dan Batch Size pada Klasifikasi Covid Menggunakan Deep Learning dengan Optimizer Adam,” J. Inf. Eng. Educ. Technol., vol. 5, no. 2, pp. 44–48, 2021, doi: 10.26740/jieet.v5n2.p44-48.
[25] D. Motta et al., “Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes,” PLoS One, vol. 15, no. 7, pp. 1–30, 2020, doi: 10.1371/journal.pone.0234959.
[26] S. Bhattacharya, A. Mukherjee, and S. Phadikar, “A Deep Learning Approach for the Classification of Rice Leaf Diseases,” Adv. Intell. Syst. Comput., vol. 1109, no. May 2020, pp. 61–69, 2020, doi: 10.1007/978-981-15-2021-1_8.
[27] A. A. J. V. Priyangka and I. M. S. Kumara, “Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method,” Lontar Komput. J. Ilm. Teknol. Inf., vol. 12, no. 2, p. 123, 2021, doi: 10.24843/lkjiti.2021.v12.i02.p06.
[28] W. Widystuti and J. B. B. Darmawan, “Pengaruh jumlah data set terhadap akurasi pengenalan dalam deep convolutional network,” Konf. Nas. Sist. Inf., pp. 8–9, 2018.
[29] 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, doi: 10.1109/CVPR.2018.00474.
[30] C. Manliguez, “Generalized Confusion Matrix for Multiple Classes,” no. November, pp. 5–7, 2016, doi: 10.13140/RG.2.2.31150.51523.
[31] D. Zhang, J. Wang, and X. Zhao, “Estimating the Uncertainty of Average F1 Scores,” in Proceedings of the 2015 International Conference on The Theory of Information Retrieval, Sep. 2015, pp. 317–320, doi: 10.1145/2808194.2809488.
[32] Faiz Nashrullah, Suryo Adhi Wibowo, and Gelar Budiman, “The Investigation of Epoch Parameters in ResNet-50 Architecture for Pornographic Classification,” J. Comput. Electron. Telecommun., vol. 1, no. 1, pp. 1–8, 2020, doi: 10.52435/complete.v1i1.51.
[33] S. N. Rahmawati, E. W. Hidayat, and H. Mubarok, “Implementasi Deep Learning Pada Pengenalan Aksara Sunda Menggunakan Metode Convolutional Neural Network,” Inser. Inf. Syst. Emerg. Technol. J., vol. 2, no. 1, p. 46, 2021, doi: 10.23887/insert.v2i1.37405.
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2022-12-13
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Afis Julianto, Andi Sunyoto, & Ferry Wahyu Wibowo. (2022). OPTIMASI HYPERPARAMETER CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT TANAMAN PADI. TEKNIMEDIA: Teknologi Informasi Dan Multimedia, 3(2), 98-105. https://doi.org/10.46764/teknimedia.v3i2.77
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