Klasifikasi Citra Bunga Dahlia Berdasarkan Warna Menggunakan Metode Otsu Thresholding Dan Naïve Bayes

Achmad Syaeful(1*), Muhammad Ilham Fadillah(2), Imam Muftadi(3), Dadang Iskandar(4),


(1) Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
(2) Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
(3) Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
(4) Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
(*) Corresponding Author

Abstract


Flowers are one of the organs of the plant body that function for generative propagation which has various forms and ways of working according to the type, but for plants that have seeds, these tools are usually important for plants that we know as flowers. Flowers are an important item in the object recognition process. The item recognition process in the computerized division is very important for determining the foundation and forefront of an image. It expects to get the spotlight it needs. The flower image in this study has a complicated picture which is very inconvenient because there are leaves and trees around the flower image. So, in this case concentrate on the proposed division involving Otsu Threshold as a strategy to isolate views and closer foundations. The division process is very firm to get shape highlights such as area, eccentricity, and perimeter. utilizing the computation of these elements will be sorted using the calculation of the Naïve Bayes algorithm by utilizing 120 flower images from 17 flower datasets. The dataset will be partitioned into test information and prepare information, and take advantage of cross-consensus (k=10). ensure that the settings using Naïve Bayes get a higher precision level of 99.168% with a relative absolute error of 9.0284%

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References


P. Rosyani and Oke Hariansyah, “Pengenalan Citra Bunga Menggunakan Segmentasi Otsu Treshold dan Naïve Bayes,” J. Sist. dan Inform., vol. 15, no. 1, pp. 1–7, 2020, doi: 10.30864/jsi.v15i1.304.

A. Tri Utami, “Implementasi Metode Otsu Thresholding untuk Segmentasi Citra Daun,” Fak. Komun. dan Inform. Univ. Muhammadiyah Surakarta, 2017.

D. M. Prayogo, K. Gunadi, and E. Setyati, “Pengenalan Jenis Bunga Anggrek Menggunakan Metode Color Local Binary Pattern dan Support Vector Machine,” J. Infra, vol. 8(1), pp. 242--248, 2020.

F. Muwardi et al., “Pengolahan Citra Dan Pengklasifikasi Jarak,” J. Ilmu Tek. Elektro Komput. dan Inform., vol. 3, no. 2, pp. 124–131, 2017.

I. Setiawan, W. Dewanta, H. A. Nugroho, and H. Supriyono, “Pengolah Citra Dengan Metode Thresholding Dengan Matlab R2014A,” J. Media Infotama, vol. 15, no. 2, 2019, doi: 10.37676/jmi.v15i2.868.

Maulana Fansyuri and O. Hariansyah, “Pengenalan Objek Bunga dengan Ekstraksi Fitur Warna dan Bentuk Menggunakan Metode Morfologi dan Naïve Bayes,” J. Sist. dan Inform., vol. 15, no. 1, pp. 70–80, 2020, doi: 10.30864/jsi.v15i1.338.

M. R. Kumaseh, L. Latumakulita, N. Nainggolan, and S. Citra, “Segmentasi Citra Digital Ikan Menggunakan Digital Fish Image Segmentation by Thresholding Method,” J. Ilm. Sains, vol. 13, 2013.

P. Rosyani and R. Amalia, “Segmentasi Citra Tanaman Obat dengan metode K-Means dan Otsu,” J. Inform. Univ. Pamulang, vol. 6, no. 2, pp. 246–251, 2021, [Online]. Available: http://openjournal.unpam.ac.id/inde x.php/informatika246.

P. Rosyani and S. Saprudin, “Deteksi Citra Bunga Menggunakan Analisis Segmentasi Fuzzy C- Means dan Otsu Threshold,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 1, pp. 29–36, 2020, doi: 10.30812/matrik.v20i1.715.

R. K. Hadi, R. Hartanto, and S. Fauziati, “Peningkatan Performa

Prediksi Daerah Potensi Penangkapan Ikan Dengan Metode Threshold Adaptif,” J. ELTIKOM, vol. 4, no. 1, pp. 48–64, 2020, doi: 10.31961/eltikom.v4i1.170.

S. I. Syafi’i, R. T. Wahyuningrum, and A. Muntasa, “Segmentasi Obyek Pada Citra Digital Menggunakan Metode Otsu Thresholding,” J. Inform., vol. 13, no. 1, pp. 1–8, 2016, doi: 10.9744/informatika.13.1.1-8.

K. Ayuningsih, Y. A. Sari, and P.P. Andikara, “Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Local Binary Pattern dengan Naïve Bayes Classifier, “ J. Pengemb. Teknol. Inf. Dan Ilmu Komput. Univ.Brawijaya, Vol. 3, No.4, pp.3166-3173,2019.

L. Genisa and D. I. Mulyana, “Implementasi Penerapan Metode C4. 5 dan Naïve Bayes Dalam Tingkat Kelulusan Akreditasi Lembaga PAUD Pada Badan Akreditasi Nasional,” J. Media …, vol. 5, pp. 1595–1604, 2021, doi: 10.30865/mib.v5i4.3267.

S. R. Raysyah, Veri Arinal, and Dadang Iskandar Mulyana, “Klasifikasi Tingkat Kematangan Buah Kopi Berdasarkan Deteksi Warna Menggunakan Metode Knn Dan Pca,” JSiI (Jurnal Sist. Informasi), vol. 8, no. 2, pp. 88–95, 2021, doi: 10.30656/jsii.v8i2.3638.

D. I. Mulyana, “Optimization of Image Classification Using the Convolutional Neural Network ( CNN ) Algorithm for Cirebon Batik Image Indonesian,” no. 12, pp. 39–46, 2021.




DOI: http://dx.doi.org/10.30645/j-sakti.v6i1.470

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