Pengelompokan Buah Jeruk menggunakan Naïve Bayes dan Gray Level Co-occurrence Matrix


Rahmat Karim Haba(1*); Kartika Chandra Pelangi(2);

(1) Universitas Ichsan Gorontalo
(2) Universitas Ichsan Gorontalo
(*) Corresponding Author

  

Abstract


Tangerines are fruits that are rich in high vitamin C content. Every orchard owner always tries to improve the quality of their plantation. In the selection of tangerines to be classified as ripe or immature at harvest time, the garden planters are already accustomed, but sometimes the farmer grouping the ripe oranges has problems such as physical limitations of the farmer, which is caused by fatigue factor. because it is still grouping with conventional systems so it is not effective and efficient in classifying ripe oranges. So from that we need a computerized system that can help gardeners in classifying ripe oranges. One of the technologies currently developing in agriculture and plantations is digital image processing using a classification system based on the texture and naïve bayes method. Based on the results that have been made, that the classification system using the Naïve Bayes method on tangerine images can be classified and obtain effective and efficient performance based on testing of 82% so that it can be implemented.


Keywords


Classification; GlCM; Naïve Bayes

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 781 times
PDF view: 340 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v12i1.494.17-24 		  

Cite

How to cite item

References


H. Prabowo, “Deteksi Kondisi Kematangan Buah Jeruk Berdasarkan Kemiripan Warna Pada Ruang Warna Rgb Berbasis Android,” vol. 3, no. 2, 2017.

M. Widyaningsih, “Identifikasi Kematangan Buah Apel Dengan Gray Level Co-Occurrence Matrix (GLCM),” J. SAINTEKOM, vol. 6, no. 1, p. 71, 2017, doi: 10.33020/saintekom.v6i1.7.

U. E. Mas’ud Effendi, Fitriyah, “Identifikasi Jenis Dan Mutu Teh Menggunakan Pengolahan Citra Digital,” J. Teknotan, vol. 11, no. 2, pp. 67–76, 2017.

Yuda Permadi and Murinto, “Buah Menggunakan Metode Ekstraksi Ciri Statistik,” J. Inform., vol. 9, no. 1, pp. 1028–1038, 2015.

A. R. Putri, “Pengolahan Citra Dengan Menggunakan Web Cam Pada Kendaraan Bergerak Di Jalan Raya,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 1, no. 01, pp. 1–6, 2016, doi: 10.29100/jipi.v1i01.18.

E. K. Ratnasari and A. Wikaningrum, “Pengenalan Jenis Buah pada Citra Menggunakan Pendekatan Klasifikasi Berdasarkan Fitur Warna Lab dan Tekstur Co- Occurrence,” J. Inf., vol. 1, no. 2, pp. 88–97, 2016.

A. Ciputra, A. Susanto, and dkk, “Dengan Algoritma Naive Bayes Dan Ekstraksi Fitur Citra Digital,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 1, pp. 465–472, 2018.

R. A. Asmara, B. S. Andjani, U. D. Rosiani, and P. Choirina, “Klasifikasi Jenis Kelamin Pada Citra Wajah Menggunakan Metode Naive Bayes,” J. Inform. Polinema, vol. 4, no. 3, p. 212, 2018, doi: 10.33795/jip.v4i3.209.

I. G. S. Rahayuda, “Identifikasi Jenis Obat Berdasarkan Gambar Logo Pada Kemasan Menggunakan Metode Naive Bayes,” Sisfo, vol. 06, no. 01, pp. 17–36, 2016, doi: 10.24089/j.sisfo.2016.09.002.

Y. P. Journal, R. A. Syifa, K. Adi, D. Fisika, and U. Diponegoro, “Analisis Tekstur Citra Mikroskopis Kanker Paru Menggunakan Metode Gray Level Co-Occurance Matrix (Glcm) Dan Tranformasi Wavelet Dengan Klasifikasi Naive Bayes,” Youngster Phys. J., vol. 5, no. 4, pp. 457–462, 2016.

F. Y. Manik and K. S. Saragih, “Klasifikasi Belimbing Menggunakan Naïve Bayes Berdasarkan Fitur Warna RGB,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 11, no. 1, p. 99, 2017, doi: 10.22146/ijccs.17838.

K. Auliasari and M. Kertaningtyas, “Studi Komparasi Klasifikasi Pola Tekstur Citra Digital Menggunakan Metode K-Means Dan Naïve Bayes,” J. Inform., vol. 18, no. 2, pp. 1–11, 2018.

S. M. Treatment et al., “Klasifikasi Buah Jeruk Menggunakan Metode Naive Bayes Berdasarkan Analisis Tekstur dan Normalisasi Warna,” vol. 1, no. 1, pp. 2374–2376, 2016.

K. Prajatama, F. E. Nugroho, A. F. Sentosa, and S. Fauziah, “Deteksi Kualitas Buah Apel Malang Manalagi Menggunakan Algoritma Naive Bayes Quality Detection Of Malang Manalagi Apple Fruit Using The Algorithm Naive Bayes Program Studi S1 Jurusan Informatika Fakultas Ilmu Komputer Universitas Pengambilan Data Ekstraks,” vol. 8, no. 1, pp. 32–38, 2019.

A. Septiarini and R. Wardoyo, “Kompleksitas Algoritma GLCM untuk Ekstraksi Ciri Tekstur pada Penyakit Glaucoma,” no. April, pp. 98–102, 2016.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Rahmat Karim Haba

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