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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Jurnas Nasional Teknologi dan Sistem Informasi JURNAL EKONOMI DAN BISNIS AIRLANGGA Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer AMIK BSI KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Telematika JISKa (Jurnal Informatika Sunan Kalijaga) Syntax Idea JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Aiti: Jurnal Teknologi Informasi
Rianto Rianto
Universitas Teknologi Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

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Journal : Telematika : Jurnal Informatika dan Teknologi Informasi

 1 
Convolutional Neural Network for Identifying Tree Species Using Stem Images Nadia Pramesti; Rianto Rianto
Telematika Vol 20, No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.8774

Abstract

Purpose: Identification of tree species based on stem images using programming assistance to design an automation tool to be able to distinguish tree species directly based on stem images from the new data entered.Design/methodology/approach: Identifying tree species is usually done using leaf images, in previous studies related to identifying tree species based on leaf images this resulted in quite high accuracy but was felt to be not optimal. In this study, we used a convolutional neural network to compare the accuracy of bar images.Findings/result: from 1000 tree trunk image data, identification was carried out using the help of python with the CNN method it can be concluded that the test results used the best acuration at epoch 25 with a value reaching 96.80%Originality/value/state of the art: Research with theme identification of tree species based on stem images using the CNN method has never been done by previous researchers. 
Co-Authors Alfin Syarifuddin Syahab Andre Daegal Arief Hermawan Aziz Perdana Demas Risdho Listianto Dewi, Septi Riana Donny Avianto Edi Priyanto Emi Munawaroh Endang Setyawati Erik Iman Heri Ujianto Erik Iman Heri Ujianto Erik Iman HU Muhammad Rizki Nadia Pramesti Radjaban, Yohanes Ratika Tulus Wahyuhana Rian Oktafiani Rian Oktafiani Rubangi Rubangi Siska Howay Widya Setia Findari