Metode Naive Bayes Mendeteksi Penyakit Citrus Vein Phloem Degeneration

Apira Nurul Putri Santoso(1*), Rina Candra Noor Santi(2),

(1) Universitas Stikubank, Semarang, Indonesia
(2) Universitas Stikubank, Semarang, Indonesia
(*) Corresponding Author

Abstract


On domestic and international markets, oranges are the largest commodity in the category of fruit imports and exports. In Indonesia alone, almost all regions of Indonesia grow citrus fruits, but these plants can be affected by a variety of other diseases, one of which is Citrus Vein Phloem Degeneration in citrus leaf veins. The disease is characterized by symptoms of yellow spots around the leaves, but Indonesia itself does not have a system that can detect the severity of the disease and farmers must be able to detect it manually based on experience. This system is expected to help farmers identify the severity of the disease from leaf images. The processing steps used include trimming, resizing, preprocessing with RGB to grayscale conversion, edge detection with Sobel, feature extraction with GLCM, and naive Bayes classification. In addition, the accuracy obtained from the test results is 67.58%.

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References


R. H. Ariesdianto, Z. E. Fitri, A. Madjid, and A. M. N. Imron, “Identifikasi Penyakit Daun Jeruk Siam Menggunakan K-Nearest Neighbor,” Jurnal Ilmu Komputer dan Informatika, vol. 1, no. 2, pp. 133–140, Dec. 2021, doi: 10.54082/jiki.14.

Setyaningsih F, Rahmawati, and Mukarlina, “Analisis Sistem Pakar Untuk Mengidentifikasi Penyakit Pada Tanaman Jeruk Siam (Citrus Nobilis Var. Microcarpa) Dari Perkebunan Desa Setapok, Kota Singkawang,” Kumpulan JurnaL Ilmu Komputer (KLIK), vol. 08, no. 2, pp. 162–171, Jun. 2021.

F. Lestari, I. Purnama, A. Sajiah, and L. Aksara, “Identifikasi Penyakit Tanaman Jeruk Siam Menggunakan Metode M-SVM,” in Seminar Nasional APTIKOM (SEMNASTIK), 2019, pp. 441–448.

M. Widyaningsih and A. Harjoko, “Identifikasi Gejala Penyakit Tanaman Jeruk Melalui Pengolahan Citra,” Jurnal Sains Komputer dan Teknologi Informasi, vol. 3, no. 2, pp. 104–113, Apr. 2021.

F. Lestari, J. Sari, Sutardi, I. Purwanti, and N. Purnama, “Deteksi Penyakit Tanaman Jeruk Siam Berdasarkan Citra Daun Menggunakan Segmentasi Warna RGB-HSV,” in Seminar Nasional Teknologi Terapan Berbasis Kearifan Lokal (SNT2BKL), 2018, pp. 276–283.

M. Widyaningsih, “Segmentasi Canny Dan Otsu Pada Citra Daun Jeruk Tidak Sehat,” in SEMNASKIT, 2015, pp. 43–48.

S. Wahyuni, E. Hariyanto, and S. Batubara, “Deteksi Penyakit Tanaman Jeruk Dengan Algoritma Radial Basis Function Network,” in Prosiding Seminar Nasional Inovasi Teknologi dan Ilmu Komputer (SNITIK), Apr. 2018, pp. 358–363.

A. Anas and A. Rizal, “Deteksi Tepi Dalam Pengolahan Citra Digital,” in Seminar Nasional TIK dan Ilmu Sosial (SocioTech), Oct. 2017, pp. 1–6.

A. Septiarini, R. Saputra, A. Tejawati, and M. Wati, “Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis Pengolahan Citra,” Jurnal Rekayasa Sistem dan Teknologi Informasi (RESTI), vol. 5, no. 5, pp. 927–935, Oct. 2021, Accessed: Jun. 21, 2022. [Online]. Available: http://jurnal.iaii.or.id.

I. Ginting, “Aplikasi Deteksi Penyakit Tumbuhan Jeruk Manis Berbasis Android Dengan Menggunakan Algoritma Bayesian Beliefnetwork,” Jurnal Pelita Informatika, vol. 8, no. 1, pp. 133–137, Jul. 2019.

A. Novitasari, E. Purwandari, and F. Coastera, “Identifikasi Citra Daun Tanaman Jeruk Dengan Local Binary Pattern Dan Moment Invariant,” Jurnal Informatika dan Komputer (JIKO), vol. 3, no. 2, pp. 76–83, Sep. 2018.

S. Zayin and H. Rakhmad, “Sistem Pakar Diagnosis Hama Dan Penyakit Tanaman Jeruk Menggunakan Metode Euclidean Distance,” Jurnal Sistem & Teknologi Informasi Indonesia (JUSTINDO), vol. 1, no. 2, pp. 123–131, Aug. 2016.

F. Akbar, A. Rais, I. Sobari, R. Zuama, and B. Rudiarto, “Analisis Performa Algoritma Naive Bayes Pada Deteksi Otomatis Citra MRI,” Jurnal Ilmu Pengetahuan Dan Teknologi Komputer, vol. 5, no. 1, pp. 37–42, Aug. 2019.

F. Nugraha, B. Irawan, and D. Midyanti, “Deteksi Penyakit Pada Tanaman Jeruk Pontianak Dengan Metode Jaringan Saraf Tiruan Backpropagation,” Jurnal Coding, Sistem Komputer Untan, vol. 4, no. 2, pp. 76–85, 2016.

L. Elfianty and J. Wahyudi, “Sistem Pakar Deteksi Penyakit Tanaman Jeruk Yang Disebabkan Oleh Bakteri,” Jurnal Teknik Informatika Unika St. Thomas (JTIUST), vol. 6, no. 2, pp. 316–324, Dec. 2021.

Suyono, R. Wati, and T. Susilowati, “Sistem Pakar Diagnosa Penyakit Dan Hama Pada Tanaman Jeruk Nipis Menggunakan Metode Forward Dan Backward Chainning Berbasis Visual Basic 6.0,” Jurnal Management Sistem Informasi Dan Teknologi (EXPERT), vol. 10, no. 1, pp. 23–28, Jun. 2020.

N. Gaol, “Sistem Pakar Mendiagnosa Penyakit Tanaman Buah Citrus (Lemon) Mengggunakan Metode Certainty Factor,” Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer), vol. 19, no. 1, pp. 1–7, Feb. 2020.




DOI: http://dx.doi.org/10.30645/jurasik.v8i1.549

DOI (PDF): http://dx.doi.org/10.30645/jurasik.v8i1.549.g527

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