title: IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) UNTUK  PENGELOMPOKAN DAUN TUMBUHAN OBAT BINAHONG (ANREDERA  CORDIFOLIA) DAN SIRIH HIJAU (PIPER BETLE)
creator: Akbar Setia , Gema Muslimin
subject: 000 Ilmu komputer, informasi dan pekerjaan umum
description: Tumbuhan obat merupakan tumbuhan yang memiliki khasiat untuk mengatasi  berbagai penyakit. Klasifikasi citra digital tumbuhan obat dapat dilakukan dengan  menggunakan beberapa algoritma, salah satu nya adalah algoritma Support Vector  Machine (SVM). Klasifikasi menggunakan algoritma ini dapat dilakukan dengan  mengekstrak beberapa fitur diantaranya, warna, tekstur dan bentuk. Hasil kinerja  klasifikasi pada penelitian ini akan dibandingkan dengan algoritma Convolutional  Neural Network (CNN). Data yang digunakan dalam penelitian ini merupakan  data primer sebanyak 450 citra binahong dan 450 citra sirih. Data terbagi menjadi  data latih dan data uji, perbandingan data latih dan data uji yang digunakan yaitu ,  70:30, 75:25, dan 80:20 dengan kombinasi jarak piksel 1,2,3,4 dan 5. Hasil terbaik  didapatkan pada perbandingan 75:25 dengan nilai akurasi 92.44%.    Kata Kunci : Tumbuhan obat, Klasifikasi, Support Vector Machine (SVM).              abstract    Medicinal plants are plants that have the properties to cure various diseases. One  of the easily found medicinal plants in Indonesia is the binahong and betel plants.  Binahong and betel plants are known to have useful properties for society. The  properties possessed by these two plants are different, so it is important to be able  to differentiate between the two leaves. Computers can differentiate between the  two leaves using machine learning, known as classification. Classification can be  performed by taking digital images of the medicinal plants. Digital image  classification can be done using several algorithms, one of which is the Support  Vector Machine (SVM) algorithm. Classification using this algorithm can be done  by extracting some features such as color, texture, and shape. The performance of  the classification results in this study will be compared with the Convolutional  Neural Network (CNN) algorithm. The data used in this study is primary data  consisting of 450 binahong and 450 betel images. The features that will be  extracted from the data will go through preprocessing stages, such as resizing,  labeling, and segmentation. The data is divided into training data and testing data,  the comparison of training data and testing data used is 70:30, 75:25, and 80:20  with a combination of pixel distances 1,2,3,4, and 5. The best result was obtained  in the comparison of 75:25 with an accuracy of 92.44%..    Keywords: Medicinal plants; Classification; Support Vector Machine (SVM).
publisher: FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM 
date: 2023-02-09
type: Skripsi
type: NonPeerReviewed
format: text
identifier: http://digilib.unila.ac.id/71780/1/ABSTRAK.pdf
format: text
identifier: http://digilib.unila.ac.id/71780/2/SKRIPSI%20FULL.pdf
format: text
identifier: http://digilib.unila.ac.id/71780/3/SKRIPSI%20TANPA%20BAB%20PEMBAHASAN.pdf
identifier:   Akbar Setia , Gema Muslimin  (2023) IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) UNTUK PENGELOMPOKAN DAUN TUMBUHAN OBAT BINAHONG (ANREDERA CORDIFOLIA) DAN SIRIH HIJAU (PIPER BETLE).  FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM , UNIVERSITAS LAMPUNG.     
relation: http://digilib.unila.ac.id/71780/