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ITS » Paper and Presentation » 51200-Teknik Informatika S2
Posted by tondoindra@gmail.com at 08/06/2015 18:56:33  •  948 Views


SEGMENTASI PENYAKIT PADA CITRA DAUN TEBU MENGGUNAKAN ARTIFICIAL BEE COLONY - FUZZY C MEANS SUPPORT VECTOR MACHINE

DISEASE SEGMENTATION OF SUGARCANE LEAF IMAGE USING PARTICLE SWARM OPTIMIZATION - FUZZY C MEANS - SUPPORT VECTOR MACHINE

Author :
MENTARI, MUSTIKA ( 5112201028 )




ABSTRAK

Penyakit pada pertanian tebu harus segera diatasi agar diperoleh produktifitas yang tinggi. Deteksi penyakit yang secara manual dilakukan oleh ahli membutuhkan waktu dan biaya yang tinggi. Oleh karena itu diperlukan otomatisasi sistem untuk mendeteksi penyakit pada tanaman tebu. Tesis ini bertujuan untuk membangun sistem yang secara otomatis mampu melakukan segmentasi citra daun tebu berpenyakit dengan metode baru Artificial Bee Colony ABC-Fuzzy C MeansFCM-Support Vector Machine SVM. Hasil proses tersebut selanjutnya digunakan pada deteksi penyakit sebagai referensi untuk ketepatan permasalahan pertanian yang membutuhkan sistem deteksi penyakit sejak dini. Segmentasi citra daun tebu berpenyakitmemiliki beberapa tahapan yaitu preprocessing pemilihan region of interest ekstraksi fitur dan segmentasi. Sebelum masuk pada proses inti dilakukan pemilihan ROI yang menunjukan dominasi area penyakit pada daun menggunakan overlappingwindow seluas 100x100 pixel. Selanjutnya metode segmentasi menggunakanABC-FCM dan SVM. Tahap pertama dari metode segmentasi ini adalah kombinasi ABC dan FCM. Hasil dari tahap pertama tersebut adalah training data yang telah terlabeli. Label data tersebut digunakan pada tahap kedua bersama dengan data testing menggunakan metode klasifikasi SVM. Metode segmentasi yang diusulkan mampu menunjukkan rata-rata akurasi yang tinggi yaitu sebesar81.


ABSTRACT

The disease in sugarcane agriculture must be addressed quickly to ensure a high productivity. Detection of the disease manually by an expert requires substantial time and cost. Therefore an automated system for detection the disease in sugarcane is required. This thesis aims to develop a system which able to automatically perform image segmentation disease in sugarcane leaves image with a novel method namely Artificial Bee Colony ABC-Fuzzy C Means FCM-Support Vector Machine SVM. The result is further used in the process of disease detection as a reference to the precisionagriculture system which requires early disease detection system. Image segmentation in sugarcane leaf image consists of several phases namely preprocessing selection of region of interest ROI feature extraction and the segmentation. Before entering the core of the process performed the selectionof ROI as the dominating presence of disease areas on the leaves using an overlapping window as wide as 100 x 100 pixels.Furthermore perform segmentation process using ABC-FCM and SVM. The first part ofsegmentation is a clustering technique thatcombines ABC and FCM. Theresults of the first part are the labelled trainingdata. Thosetraining data are used in the second phase together with the testingdata using SVM classification method. The proposed segmentation method shows a high rate accuracyof 81.



KeywordsSegmentasi penyakit, daun tebu, ABC, FCM, SVM
 
Subject:  Sistem Pengoperasian
Contributor
  1. Dr. Ir. R.V. Hari Ginardi, M.Sc.
  2. Dr. Eng. Chastine Fatichah, S.Kom, M.Kom
Date Create: 17/07/2014
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-paper-51121150007344
Collection ID: 51121150007344
Call Number: RTIf 006.42 Men s


Source
Paper And Presentation of Informatics Engineering informatics Engineering RTIf 006.42 Men s, 2015, 2015

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Copyright @2015 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




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ITS-paper-51121150007344-37793.pdf




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