* Corresponding Author
Hide
Show all
Klasifikasi Belimbing Menggunakan Naïve Bayes Berdasarkan Fitur Warna RGB
https://doi.org/10.22146/ijccs.17838 Fuzy Yustika Manik(1), Kana Saputra Saragih(2*)
(1) STMIK Kaputama, Binjai
(2) Universitas Pembangunan Panca Budi Medan
(*) Corresponding Author
Abstract
Post harvest issues on star fruit are produced on a large scale or industry is sorting. Currently, star fruit classified by rind color analysis visually human eye. This method does not effective and inefficient. The research aims to classify the starfruit sweetness level by using image processing techniques. Features extraction used is the value of Red, Green and Blue (RGB) to obtain the characteristics of the color image. Then the feature extraction results used to classify the star fruit with Naïve Bayes method. Starfruit image data used 120 consisting of 90 training data and 30 testing data. The results showed the classification accuracy using RGB feature extraction by 80%. The use of RGB as the color feature extraction can not be used entirely as a feature of the image extraction of star fruit.
Keywords
Full Text:
PDFReferences
[1]
Sugianto S dan Wibowo F, 2015, Klasifikasi Tingkat Kematangan Buah Pepaya (Carica Papaya L) California (Callina-Ipb 9) Dalam Ruang Warna HSV dan Algoritma K-Nearest Neighbor, Prosiding Senatek, Purwokerto, 28 November
[2]
Zaki F, 2009, Pengembangan Probabilistic Neural Networks Untik Penentuan Kematangan Belimbing Manis, Skripsi, Jurusan Ilmu Komputer, Institut Pertanian Bogor, Bogor.
[3]
Abdullah M.Z, M Saleh J, Syahir F.A.S, dan M Azemi B.M.N, 2006, Discrimination and Classification of Fresh-cut Starfruits (Averrhoa Carambola L) using Automated Machine Vision System, Jurnal of Food Engeneering
[4]
Saputra K dan Manik F.Y,2016, Klasifikasi Belimbing Menggunakan K-Nearest Neighbors (KNN) Berdasarkan Citra Red-Green-Blue (RGB), Prosiding SEMMAU 2016, Kupang, 17 September
[5]
Liontoni F dan Nugroho H. 2015. Klasifikasi Klasifikasi Daun Herbal Menggunakan
Metode Naïve Bayes Classifier Dan Knearest Neighbor. Jurnal Simantec
[6]
Anandita E.R, 2014, Klasifikasi Tebu Dengan Menggunakan Algoritma Naïve Bayes Classification Pada Dinas Kehutanan dan Perkebunan Pati, Skripsi, Jurusan Sistem Informasi, Universitas Dian Nuswantoro, Semarang.
[7]
Balai Pengkajian Teknologi Pertanian, 2008, Pupuk & Pemupukan Tanaman Belimbing, Jakarta.
[8]
Yulandari S, 2013, Hubungan Tingkat Pengetahuan Dengan Tingkat Konsumsi Buah dan Sayur Pada Anak Kelas IV-V SD Pertiwi, Skripsi, Fakultas Keperawatan, Universitas Andalas,Padang.
[9]
Han J, Kamber M, Pei J, 2012, Data Mining: Concepts and Techniques. 3th ed, New York (US): Morgan Kaufman Elsevier Academic Pr.
[10]
Nugroho A dan Subahar, 2013, Klasifikasi Naïve Bayes Untuk Prediksi Kelahiran Pada Data Ibu Hamil. Berkala Mipa, Vol 23, Ed 3.
[11]
Prasetyo E, 2012, Data Mining : Konsep dan Aplikasi Menggunakan Matlab, Penerbit Andi, Yogyakarta
[12]
Tan PN, Steinbach M, Kumar V, 2005, Introduction to data mining, New York (US): Addison Wesley
DOI: https://doi.org/10.22146/ijccs.17838
Article Metrics
Abstract views : 15173
| 
views : 22719
Refbacks
- There are currently no refbacks.
Copyright (c) 2017 IJCCS - Indonesian Journal of Computing and Cybernetics Systems
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1