Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN

Alya Zalvadila

Abstract


Shallots are one of the most widely produced crops in Enrekang Regency. The obstacle in cultivation is the presence of disease in the plant which can reduce production yields. We can recognize this disease from the spots on the leaves because these spots have unique color and texture characteristics. The aim of this research is to determine the results of the classification of shallot plant diseases which focuses on purple spot and moler disease. The classification algorithms used are CNN and SVM with RBF, linear, sigmoid and polynomial kernels. The feature extraction method used is Gray Level Co-occurance Matrix (GLCM). The analysis was carried out using 320 datasets with 2 classes, namely, purple spot disease and moler disease, each class has 160 datasets. The test results show that the CNN and SVM methods with RBF, linear and polynomial kernels get accuracy, precision, recall and F1 scores of 100% respectively. Meanwhile, the SVM method on the sigmoid kernel using texture feature extraction with the GLCM method states that the accuracy value is 75%, precision 75%, recall 73% and F1-Score 74%. So these results state that the Sigmoid method using GLCM feature extraction has the lowest value among other methods

Keywords


Klasifikasi; Bawang Merah; SVM; CNN; GLCM

Full Text:

References


R. Pertumbuhan et al., “Skripsi Disusun Oleh : Fakultas Pertanian,” 2019.

M. K. Khamdani, N. Hidayat, and R. K. Dewi, “Implementasi Metode K-Nearest Neighbor Untuk Mendiagnosis Penyakit Tanaman Bawang Merah,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 1, pp. 11–16, 2021.

D. Aldo, “Sistem Pakar Diagnosis Hama Dan Penyakit Bawang Merah Menggunakan Metode Dempster Shafer,” Komputika J. Sist. Komput., vol. 9, no. 2, pp. 85–93, 2020, doi: 10.34010/komputika.v9i2.2884.

F. Felix, S. Faisal, T. F. M. Butarbutar, and P. Sirait, “Implementasi CNN dan SVM untuk Identifikasi Penyakit Tomat via Daun,” J. SIFO Mikroskil, vol. 20, no. 2, pp. 117–134, 2019, doi: 10.55601/jsm.v20i2.670.

P. N. Andono and E. H. Rachmawanto, “Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 1–9, 2021, doi: 10.29207/resti.v5i1.2615.

G. T. Situmorang, A. W. Widodo, and M. A. Rahman, “Penerapan Metode Gray Level Co-occurrence Matrix ( GLCM ) untuk ekstraksi ciri pada telapak tangan,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 5, pp. 4710–4716, 2019.

B. Nugroho and E. Y. Puspaningrum, “Kinerja Metode CNN untuk Klasifikasi Pneumonia dengan Variasi Ukuran Citra Input,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 3, p. 533, 2021, doi: 10.25126/jtiik.2021834515.

C. Wijaya, H. Irsyad, and W. Widhiarso, “Klasifikasi Pneumonia Menggunakan Metode K-Nearest Neighbor Dengan Ekstraksi Glcm,” J. Algoritm., vol. 1, no. 1, pp. 33–44, 2020, doi: 10.35957/algoritme.v1i1.431.

D. Alita, Y. Fernando, and H. Sulistiani, “Implementasi Algoritma Multiclass Svm Pada Opini Publik Berbahasa Indonesia Di Twitter,” J. Tekno Kompak, vol. 14, no. 2, p. 86, 2020, doi: 10.33365/jtk.v14i2.792.

P. U. Rakhmawati, Y. M. Pranoto, and E. Setyati, “Klasifikasi Penyakit Daun Kentang Berdasarkan Fitur Tekstur dan Fitur Warna Menggunakan Support Vector Machine,” Semin. Nas. Teknol. dan Rekayasa, pp. 1–8, 2018.

M. Muhathir, M. H. Santoso, and D. A. Larasati, “Wayang Image Classification Using SVM Method and GLCM Feature Extraction,” J. Informatics Telecommun. Eng., vol. 4, no. 2, pp. 373–382, 2021, doi: 10.31289/jite.v4i2.4524.

Y. Prastyaningsih, “Kombinasi Fitur Multi-Scale Gray Level Co-Occurrence Matrices dan Warna Untuk Sistem Temu Kembali Citra Gerabah,” p. 11, 2016.

E. Rasywir, R. Sinaga, and Y. Pratama, “Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN),” Paradig. - J. Komput. dan Inform., vol. 22, no. 2, pp. 117–123, 2020, doi: 10.31294/p.v22i2.8907.

M. R. Alwanda, R. P. K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” J. Algoritm., vol. 1, no. 1, pp. 45–56, 2020, doi: 10.35957/algoritme.v1i1.434.

S. Yuliany, Aradea, and Andi Nur Rachman, “Implementasi Deep Learning pada Sistem Klasifikasi Hama Tanaman Padi Menggunakan Metode Convolutional Neural Network (CNN),” J. Buana Inform., vol. 13, no. 1, pp. 54–65, 2022, doi: 10.24002/jbi.v13i1.5022.

I. Perlindungan and Risnawati, “Pengenalan Tanaman Cabai Dengan Teknik Klasifikasi Menggunakan Metode CNN,” Semin. Nas. Mhs. ilmu Komput. dan Apl., pp. 15–22, 2020.

cktavia N. Putri, “Implementasi Metode CNN Dalam Klasifikasi Gambar Jamur Pada Analisis Image Processing (Studi Kasus: Gambar Jamur Dengan Genus Agaricus Dan Amanita),” pp. 1–80, 2020, [Online]. Available: https://dspace.uii.ac.id/bitstream/handle/123456789/23677/16611103 Ocktavia Nurima Putri.pdf?sequence=1&isAllowed=y.

S. Muhammad and A. T. Wibowo, “Klasifikasi Tanaman Aglaonema Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network (Cnn,” e-Proceeding Eng., vol. 8, no. 5, pp. 10621–10636, 2021.

V. No, P. R. Prayoga, T. Hasanuddin, and H. Darwis, “Edumatic : Jurnal Pendidikan Informatika Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Support Vector Machine dengan Fitur Fourier Descriptor,” vol. 7, no. 1, pp. 160–168, 2023, doi: 10.29408/edumatic.v7i1.17521.

A. M. Puspitasari, D. E. Ratnawati, and A. W. Widodo, “Klasifikasi Penyakit Gigi Dan Mulut Menggunakan Metode Support Vector Machine,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 2, pp. 802–810, 2018, [Online]. Available: http://j-ptiik.ub.ac.id.




DOI: https://doi.org/10.30591/jpit.v8i3.5341

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Terindeks oleh :