Article Per Year (5 Year)
p-Index From 2019 - 2024
1.225
P-Index
This Author published in this journals
All Journal Perfecting a Video Game with Game Metrics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Filsafat Indonesia Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service
Agus Ambarwari
Politeknik Negeri Lampung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Perfecting a Video Game with Game Metrics

 1 
Plant species identification based on leaf venation features using SVM Agus Ambarwari; Qadhli Jafar Adrian; Yeni Herdiyeni; Irman Hermadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14062

Abstract

The purpose of this study is to identify plant species using leaf venation features. Leaf venation features were obtained through the extraction of leaf venation features. The leaf image segmentation was performed to obtain the binary image of the leaf venation which is then determined the branching point and ending point. From these points, the extraction of leaf venation feature was performed by calculating the value of straightness, a different angle, length ratio, scale projection, skeleton length, number of segments, total skeleton length, number of branching points and number of ending points. So that from the extraction of leaf venation features 19 features were obtained. Identification of plant species was carried out using Support Vector Machine (SVM) with RBF kernel. The learning model was built using 75% of the training data. The testing results using 25% of the data on the training model, obtained an accuracy of 82.67%, with an average of precision of 84% and recall of 83%. 
Co-Authors Anung Wahyudi Dewi Kania Widyawati Dimitri Mahayana Emir Mauludi Husni Imam Ahmad Iqbal Yasin Irman Hermadi Muhammad Adam Putra Darma Neneng Neneng Qadhli Jafar Adrian Rohmat Indra Borman Yusmaida Yusmaida Yusra Fernando