Sistem Pakar Diagnosa Penyakit Pada Tanaman Sawi Menggunakan Metode Convolutional Neural Network Berbasis Android


Authors

  • Muhammad Yusuf Universitas Muhammadiyah Sorong, Sorong, Indonesia
  • Syamsudin Aliphadji Talaohu Universitas Muhammadiyah Sorong, Sorong, Indonesia
  • Jumria Purnamasari Universitas Muhammadiyah Sorong, Sorong , Indonesia

DOI:

https://doi.org/10.30865/klik.v5i1.2031

Keywords:

Mustard; Expert System; Deep Learning; CNN; Android

Abstract

Mustard greens are one of the vegetable crops that are very easy to cultivate, because they are able to grow in the highlands and lowlands, mustard greens are also susceptible to diseases that can reduce yields and quality, identifying mustard diseases manually is difficult and requires in-depth knowledge of the symptoms. and causes of disease in mustard plants. The aim of this research is to build a system that can be used to identify mustard plant diseases. The diseases used consist of 3 types, namely, leaf miner, leaf rot, and armyworm. With current technological developments that can help farmers minimize errors in determining diseases in mustard plants, Deep Learning is a field of machine learning that utilizes artificial neural networks to solve problems with large datasets. One algorithm that is often used in deep learning systems is Convolutional Neural Network (CNN). The results obtained with the dataset used were with the highest accuracy of 100% train accuracy and 97.78% validation accuracy, and obtained the highest accuracy results using f1-score, namely 95.56%.

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References

M. D. Sartika, W. D. Andika, and S. Sumarni, “Literature Review?: Motivasi yang Diberikan Kepada Anak Dalam Mengkonsumsi Sayuran,” J. Pendidik. Anak, Vol. 11 (1), 2022, Hal. 30-39, vol. 11, no. 1, pp. 30–39, 2022.

N. S. Damayanti, D. W. Widjajanto, and S. Sutarno, “Pertumbuhan dan produksi tanaman sawi Pakcoy (Brassica rapa l.) akibat dibudidayakan pada berbagai media tanam dan dosis pupuk organik,” J. Agro Complex, vol. 3, no. 3, p. 142, 2019, doi: 10.14710/joac.3.3.142-150.

M. Delfiya and N. Ariska, “Pengaruh Kombinasi Media Tanam Terhadap Pertumbuhan dan Hasil Tanaman Sawi (Brassica Juncea L.),” COMSERVA Indones. J. Community Serv. Dev., vol. 1, no. 9, pp. 614–622, 2022, doi: 10.36418/comserva.v1i9.124.

Yuli Ataribaba, Petrus Selestinus Peten, and Carolina Diana Mual, “Pengaruh Pupuk Hayati terhadap Pertumbuhan Tanaman Sawi (Brassica juncea L.) di Kampung Sidomulyo, Distrik Oransbari, Kabupaten Manokawari Selatan, Provinsi Papua Barat,” J. Trit., vol. 12, no. 2, pp. 66–78, 2021, doi: 10.47687/jt.v12i2.215.

H. Sastypratiwi and R. D. Nyoto, “Analisis Data Artikel Sistem Pakar Menggunakan Metode Systematic Review,” J. Edukasi dan Penelit. Inform., vol. 6, no. 2, p. 250, 2020, doi: 10.26418/jp.v6i2.40914.

M. Krichen, “Convolutional Neural Networks: A Survey,” Computers, vol. 12, no. 8, pp. 1–41, 2023, doi: 10.3390/computers12080151.

A. Permana and K. Budayawan, “Aplikasi Android Pengklasifikasi Semantik Teks Menggunakan Tensorflow Lite Pada Ringkasan Karya Ilmiah,” Voteteknika (Vocational Tek. Elektron. dan Inform., vol. 8, no. 4, p. 128, 2020, doi: 10.24036/voteteknika.v8i4.110349.

M. Liu, H. Deng, and W. Dong, “Identification of Mangrove Invasive Plant Derris Trifoliate Using UAV Images and Deep Learning Algorithms,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 10017–10026, 2022, doi: 10.1109/JSTARS.2022.3223227.

X. Wu, Z. Zhao, R. Tian, S. Gao, Y. Niu, and H. Liu, “Exploration of total synchronous fluorescence spectroscopy combined with pre-trained convolutional neural network in the identification and quantification of vegetable oil,” Food Chem., vol. 335, no. July 2020, p. 127640, 2021, doi: 10.1016/j.foodchem.2020.127640.

A. Irfansyah, D., Mustikasari, M., & Suroso, “Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi,” J. Inform. J. Pengemb. IT, vol. 6, no. 2, pp. 87–92, 2021.

E. I. Haksoro and A. Setiawan, “Pengenalan Jamur Yang Dapat Dikonsumsi Menggunakan Metode Transfer Learning Pada Convolutional Neural Network,” J. ELTIKOM, vol. 5, no. 2, pp. 81–91, 2021, doi: 10.31961/eltikom.v5i2.428.

D. Husen, K. Kusrini, and K. Kusnawi, “Deteksi Hama Pada Daun Apel Menggunakan Algoritma Convolutional Neural Network,” J. Media Inform. Budidarma, vol. 6, no. 4, p. 2103, 2022, doi: 10.30865/mib.v6i4.4667.

M. Megawaty and N. Huda, “Pembaharuan Sistem Penentuan Untuk Klasifikasi Jenis Penyakit pada RSUD Sekayu Menggunakan Pendekatan Extreme Programming,” J. MEDIA Inform. BUDIDARMA, vol. 5, no. 1, p. 66, Jan. 2021, doi: 10.30865/mib.v5i1.2273.

H. Fonda, “KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN): KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN),” J. Ilmu Komput., vol. 9, no. 1 SE-Articles, pp. 7–10, May 2020, doi: 10.33060/JIK/2020/Vol9.Iss1.144.

F. N. Cahya, N. Hardi, D. Riana, and S. Hadiyanti, “Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network (CNN),” Sistemasi, vol. 10, no. 3, p. 618, 2021, doi: 10.32520/stmsi.v10i3.1248.

F. F. Maulana and N. Rochmawati, “Klasifikasi Citra Buah Menggunakan Convolutional Neural Network,” J. Informatics Comput. Sci., vol. 1, no. 02, pp. 104–108, 2020, doi: 10.26740/jinacs.v1n02.p104-108.

F. Mahardika, M. Khoiri, and M. Al ‘Amin, “Implementasi Extreme Programing pada Sistem Informasi Penggajian untuk Peningkatan Pelayanan kepada Karyawan,” Hello World J. Ilmu Komput., vol. 2, no. 2, pp. 74–84, 2023, doi: 10.56211/helloworld.v2i2.274.

I. Tarsini and R. Anggraeni, “Explore flowchart and pseudocode concepts in algorithms and programming,” Indones. J. Multidiscip. Sci., vol. 3, no. 5, 2024, doi: 10.55324/ijoms.v3i5.807.

R. Soekarta, S. Aras, and Ahmad Nur Aswad, “Hyperparameter Optimization of CNN Classifier for Music Genre Classification,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 5, pp. 1205–1210, 2023, doi: 10.29207/resti.v7i5.5319.

P. S. Uttarwar, R. P. Tidke, D. S. Dandwate, and U. J. Tupe, “A Literature Review on Android -A Mobile Operating System,” Int. Res. J. Eng. Technol., vol. 8, no. 1, pp. 1–6, 2021


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Published: 2024-08-14
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