IMPLEMENTASI SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI VARIETAS TANAMAN KELENGKENG BERDASARKAN CITRA DAUN MENGGUNAKAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX

Anggita, Dhea (2024) IMPLEMENTASI SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI VARIETAS TANAMAN KELENGKENG BERDASARKAN CITRA DAUN MENGGUNAKAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX. Other thesis, UPN Veteran Yogyajarta.

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Abstract

viii
ABSTRAK
Tanaman kelengkeng (Dimarcopus Longan) merupakan tanaman yang berasal dari
Asia Tenggara dan termasuk dalam kelompok buah leci dan rambutan. Tanaman
kelengkeng yang tumbuh di jawa biasanya berasal dari Thailand dan Vietnam, namun
kelengkeng asli Indonesia berasal dari Kalimantan yang dikenal dengan buah ihau.
Kelengkeng sendiri memiliki nilai yang cukup tinggi di pasar Indonesia. Rasanya yang
manis, aromanya yang khas, mudah dikupas, kaya akan vitamin dan serat membuat buah
ini sangat digemari oleh semua kalangan. Petani Indonesia telah berhasil mengembangkan
beberapa jenis kelengkeng dari Thailand dan Vietnam, atau persilangan dari keduanya,
yang dapat tumbuh dan berkembang di dataran tinggi maupun dataran rendah.
Metode Gray Level Co-occurance Matrix (GLCM) adalah metode yang digunakan
pada penelitian ini untuk ekstraksi fitur tekstur pada citra daun kelengkeng, penelitian ini
juga menggunakan metode Support Vector Machine (SVM) untuk proses klasifikasi
varietas tanaman kelengkeng berdasarkan citra daun menggunakan fitur yang diperoleh
dari proses ekstraksi fitur tekstur menggunakan metode GLCM dan ekstraksi warna RGB.
Sistem dirancang menggunakan bahasa pemrograman python. Varietas tanaman
kelengkeng yang diklasifikasikan adalah kelengkeng matalada, kristal, merah dan
pingpong.
Hasil penelitian menggunakan metode GLCM dan SVM mampu
mengklasifikasikan varietas tanaman kelengkeng dengan empat kelas dan menghasilkan
akurasi training tertinggi sebesar 94,37%, dan akurasi testing sebesar 95%. Dari hasil
pengujian sistem tersebut, dapat disimpulkan bahwa sistem berjalan dengan baik dalam
mengklasifikasikan varietas tanaman kelengkeng berdasarkan citra daun.
Kata Kunci : Varietas Tanaman Kelengkeng, Gray Level Co-occurance Matrix,
Support Vector Machine
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ABSTRACT
The longan plant (Dimarcopus Longan) is a plant that originates from Southeast
Asia and belongs to the lychee and rambutan fruit groups. The longan plant that grows in
Java usually comes from Thailand and Vietnam, but the original Indonesian longan comes
from Kalimantan, which is known as the ihau fruit. Longan itself has a fairly high value in
the Indonesian market. Its sweet taste, distinctive aroma, easy to peel, rich in vitamins and
fiber make this fruit very popular with all people. Indonesian farmers have succeeded in
developing several types of longan from Thailand and Vietnam, or crosses of the two,
which can grow and develop in the highlands and lowlands.
The Gray Level Co-occurance Matrix (GLCM) method is the method used in this
study for the extraction of texture features in longan leaf images, this study also uses the
Support Vector Machine (SVM) method for the process of classifying longan plant
varieties based on leaf images using the features obtained from the texture feature
extraction process using the GLCM method and RGB color extraction. The system is
designed using the Python programming language. The varieties of longan plants that are
classified are longan matalada, crystal, red and ping pong.
The results of the study using the GLCM and SVM methods were able to classify
longan plant varieties with four classes and produced the highest training accuracy of
94.37%, and testing accuracy of 95%. From the results of testing the system, it can be
concluded that the system works well in classifying longan plant varieties based on leaf
images.
Keyword : Longan Plant Varieties, Gray Level Co-occurance Matrix, Support Vector
Machine

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Longan Plant Varieties, Gray Level Co-occurance Matrix, Support Vector Machine
Subjects:	Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Eko Yuli
Date Deposited:	06 Feb 2024 02:21
Last Modified:	06 Feb 2024 02:27
URI:	http://eprints.upnyk.ac.id/id/eprint/38843

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