Implementasi Transfer Learning Dalam Klasifikasi Penyakit Pada Daun Teh Menggunakan MobileNetV2
Abstract
Indonesia is currently the seventh largest tea producer in the world. However, tea farmers in Indonesia still use old technology and simple farming methods. Limited knowledge of small farmers about diseases that can attack their tea leaves. a tool is needed to classify the types of tea leaf diseases using digital images. This research uses a deep learning approach with Convolutional Neural Network using MobileNetV2 architecture for tea leaf disease classification with digital image data. With the transfer learning method, the MobileNetV2 model is trained with 3 different epochs. The best accuracy is obtained from the model trained with epoch 20 with an accuracy value of 94.6% with a loss value of 0.287. The MobileNetV2 model that has been trained shows good results in classifying tea leaf diseases.
References
[2] A.K. Pandey, G.D. Sinniah, A. Babu and A. Tanti. "How The Global Industry Copes With Fungal Diseases - Challenges and Opportunities". 2021 The American Phytopathological Society, vol. 105, no. 7, p 1868-1879, 2021
[3] N. Ibrahum, et al. “Klasifikasi Tingkat Kematangan Pucuk Daun Teh Menggunakan Metode Convolutional Neural Network”. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 10, no. 1, p. 162-176, 2022
[4] F. Mashuri and U. Enri. “Implementasi Transfer Learning Dalam Mendeteksi Penyakit Pada Gandum”. Jurnal Nuansa Informatika, vol. 16, no. 1, p 66-77, 2022
[5] Herwina, Darmatasia, A. K. A. Shiddiq and T. D. Syahputra. “Deteksi Penyakit pada Tanaman Padi Menggunakan MobileNet Transfer Learning Berbasis Android”. AGENTS: Journal of Artificial Intelligence & Data Science, vol. 2, no. 2, p. 1-8, 2022
[6] S. Datta, “Tea_Leaf_Disease”, 22 May 2020. [Online]. Available: https://www.kaggle.com/datasets/saikatdatta1994/tea-leaf-disease . [Accessed on 25 September 2022]
[7] R. O. Ekoputris, "MobileNet: Deteksi Objek pada Platform Mobile", 9 May 2018.[Online]. Available: https://medium.com/nodeflux/mobilenet-deteksi-objek-pada-platform-mobile-bbbf3806e4b3 . [Access on 27 September 2022]
[8] N. Rochmawati, dkk. "Analisa Learning rate dan Batch size Pada Klasifikasi Covid Menggunakan Deep learning dengan Optimizer Adam". Journal Information Engineering and Educational Technology, vol. 05, no. 02, p. 44-48, 2021
[9] Q. Lina, "Apa itu Convolutional Neural Network?", 2 Januari 2019.[Online]. Available: https://medium.com/@16611110/apa-itu-convolutional-neural-network-836f70b193a4 . [Access on 28 September 2022]
[10] R.R. Allaam and A.T. Wibowo. “Klasifikasi Genus Tanaman Anggrek Menggunakan Metode Convolutional Neural Network (CNN)”. e-Proceeding of Engineering, vol. 8, no. 2, p. 1153-1189, 2021
[11] A. Fuadi and A. Suharso. “Perbandingan Arsitektur Mobilenet Dan Nasnetmodile Untuk Klasifikasi Penyakit Pada Citra Daun Kentang”. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 07, no. 03, 2022
[12] M.F. Supriadi, E. Rachmawati and A. Arifianto. “Pembangunan Aplikasi Mobile Pengenalan Objek Untuk Pendidikan Anak Usia Dini”. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 8, no. 2, p. 357-364, 2019
[13] M. Basorudin, A. Rizqi, S. Murdaningrum and W. Maharani. “Kajian Persebaran Komoditas Teh: Pengembangan Kawasan Perkebunan Teh di Provinsi Jawa Barat Tahun 2015”. Jurnal Sosial Ekonomi Pertanian, vol. 15, no. 5, p. 205-214, 2019
[14] E.I. Haksoro and A. Setiawan. “Pengenalan Jamur Yang Dapat Dikonsumsi Menggunakan Metode Transfer Learning Pada Convolutional Neural Network”. Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer, vol. 5, no. 2, p. 81-91, 2021
[15] N. Hardi. "Komparasi Algoritma MobileNet Dan Nasnet Mobile Pada Klasifikasi Penyakit Daun Teh". Reputasi: Jurnal Rekayasa Perangkat Lunak, vol. 3, no. 1, p. 50-56, 2022
This work is licensed under a Creative Commons Attribution 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, the copyright of the article shall be assigned to JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) as the publisher of the journal. Copyright encompasses exclusive rights to reproduce and deliver the article in all forms and media, as well as translations. The reproduction of any part of this journal (printed or online) will be allowed only with written permission from JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya). The Editorial Board of JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) makes every effort to ensure that no wrong or misleading data, opinions, or statements be published in the journal.
This work is licensed under a Creative Commons Attribution 4.0 International License.
