Klasifikasi Penyakit Daun Padi Berdasarkan Hasil Ekstraksi Fitur GLCM Interval 4 Sudut

Jani Kusanti, Noor Abdul Haris

Abstract


One of the factors causing rice production depression is a typical disease in rice plants. Typical of disease in rice plants, among others, such Blast Disease, Leaf Blight Disease, Disease Hawar On Stem, Crackle Disease and so on. Each type of disease requires different treatment, but not all farmers know the type of disease so as to allow for errors in the handling. This research made an application program that can identify rice pests to facilitate farmers solve the problems of rice plants disease since it becomes important to make a disease classification system on the leaves of rice plants. This research uses backpropagation method to classify the type of disease resulting from feature extraction of GLCM with 4 angles. Results obtained 80% accuracy from 30 data, with 16 seconds testing time.


 


Full Text:

References


Tupamahu,F., Christyowidiasmoro, dan Purnomo, M.H., 2014, Ekstraksi Fitur Citra untuk Klasifikasi Penyakit Pada Daun Tanaman Jagung Berdasarkan Tekstur dan Warna, SNAST ( Seminar Nasional Aplikasi Sains dan Teknologi) Yogyakarta, pp.A1-A8

Kailey,K.S., & Sahdra,G.S., 2012. Content Based Image Retrieval (CBIR) for Identifying Image Based Plant Disease, IJCTA, Vol 3(3), pp.1099-1104.

Adnan,et.all., 2015, Identifikasi Varietas Padi Menggunakan Pengolahan Citra Digital dan Analisis Diskriminan, Jurnal Penelitian Pertanian Tanaman Pangan, 34(2), pp.90-96.

Arthalia, I. & Suharjo, R., 2016., Sistem Identifikasi Penyakit pada Tanaman Padi., jurnal Komputasi, FMIPA-UNILA, 4(1), pp.9–18.

Irsan, M., Pratama, V.N., dan Fakih, M., 2015. Sistem Pakar Identifikasi Tanaman Padi di Balai Penyuluhan Pertanian Sepatan Tangerang, STIMIK, STIKOM, Bali, pp. 284-289.

Yulianto, Setiadi, A., Firmansyah, I., Maulana, I., Asmoro, D., dan Kamal,H., 2015., Model Sistem Pakar Diagnosa Hama Tanaman Padi Untuk Memberikan Solusi Penanggulangan, Seminar Nasional Teknologi Informasi dan Multimedia, STMIK AMIKOM Yogyakarta, pp.3.6.7-3.6.12.

Zahrah, S., Saptono, R. dan Suryani, E., 2016. Identifikasi Gejala Penyakit Padi Menggunakan Operasi Morfologi Citra. SNIK, UNNES, pp.100–106.

Rachmat,M., 2013, Diversifikasi Pangan dan Transformasi Pembangunan Pertanian, Badan Penelitian dan Pengembangan Pertanian Kementrian Pertanian, Jakarta. https://www.neliti.com/id/balitbangtan?per_page=100&page=6, (diakses : Pebruari 12, 2017)

Gonzalez, R.C., Wood, R.E., 2004, Digital Image Processing Second Edition, Prentice Hall, New Jersey.

Kadir, A. & Susanto, A., 2013, Teori dan Aplikasi Pengolahan Citra, Andi Offset, Yogyakarta.

Hagan,M.T., Demuth,H.B., Beale,M.H., De Jesús,O.,1996, Neural Network Design 2nd Edition, on Amazon.com.




DOI: http://dx.doi.org/10.30591/jpit.v3i1.669

Refbacks

  • There are currently no refbacks.


Terindeks oleh :

 

 

http://ejournal.poltektegal.ac.id/public/site/images/informatika/logoGaruda-kecil1.png

 

 

 http://ejournal.poltektegal.ac.id/public/site/images/informatika/Google_Scholar_logo.png

 

 

 

DRJI Indexed Journal

 

 

 

 

 

 

 

 

 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Tim Redaksi JURNAL INFORMATIKA : JURNAL PENGEMBANGAN IT

Program Studi D4 Teknik Informatika
Politeknik Harapan Bersama Tegal
Jl. Mataram No.09 Pesurungan Lor Kota Tegal

Telp. +62283 - 352000

Email :
informatika.ejournal@poltektegal.ac.id

   

Copyright: JPIT (Jurnal Informatika: Jurnal Pengembangan IT) p-ISSN: 2477-5126 (print), e-ISSN 2548-9356 (online) 

Flag Counter
 
 
 
 
site
stats
 
View Visitor Statistic
 
 
 
 
 

 

Creative Commons License
JPIT (Jurnal Informatika: Jurnal Pengembangan IT) is licensed under a Creative Commons Attribution 4.0 International License.