Convolutional Neural Network for Identifying Tree Species Using Stem Images

Nadia Pramesti, Rianto Rianto

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. 

Keywords


accuracy; CNN; Epoch; Identification

Full Text:

PDF

References


V. Ansari and E. Prianto, “Prosiding SNST ke-5 Tahun 2014 Fakultas Teknik Universitas Wahid Hasyim Semarang 1,” vol. 2014, no. Pp 101, pp. 1–6, 2021.

O. D. S. Sunanto and P. H. Utomo, “Implementasi Deep Learning Dengan Convolutional Neural Network Untuk Klasifikasi Gambar Sampah Organik Dan Anorganik,” Pattimura Proceeding Conf. Sci. Technol., vol. 1, no. 2, pp. 335–340, 2022.

S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018.

R. Naquitasia, D. H. Fudholi, and L. Iswari, “Analisis Sentimen Berbasis Aspek pada Wisata Halal dengan Metode Deep Learning,” J. Teknoinfo, vol. 16, no. 2, p. 156, 2022, doi: 10.33365/jti.v16i2.1516.

D. Irfansyah et al., “Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi,” vol. 6, no. 2, 2021.

K. Heryandi Suradiradja, U. Pamulang, J. Raya Puspiptek, K. Pamulang, and K. Tangerang Selatan, “Algoritme Machine Learning Multi-Layer Perceptron dan Recurrent Neural Network untuk Prediksi Harga Cabai Merah Besar di Kota Tangerang,” Fakt. Exacta, vol. 14, no. 4, pp. 1979–276, 2021, doi: 10.30998/faktorexacta.v14i4.10376.

M. F. Naufal and S. F. Kusuma, “Pendeteksi Citra Masker Wajah Menggunakan CNN dan Transfer Learning,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 6, p. 1293, 2021, doi: 10.25126/jtiik.2021865201.

J. S. W. Hutauruk, T. Matulatan, and N. Hayaty, “Deteksi Kendaraan secara Real Time menggunakan Metode YOLO Berbasis Android,” J. Sustain. J. Has. Penelit. dan Ind. Terap., vol. 9, no. 1, pp. 8–14, 2020, doi: 10.31629/sustainable.v9i1.1401.

L. Marifatul Azizah, S. Fadillah Umayah, and F. Fajar, “Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer,” Semesta Tek., vol. 21, no. 2, pp. 230–236, 2018, doi: 10.18196/st.212229.

B. Vania Sianto and S. Handayani Tambun, “UJI AKTIVITAS ANTIKOLESTEROL KOMBINASI EKSTRAK DAUN AFRIKA (Vernonia amygdalina) DAN DAUN PINUS (Pinus merkusii) SECARA IN VITRO,” FARMASI, 2022.

E. R. Sitepu, “Motif Pinus Merkusii Dalam Karya Batik Kain Panjang,” Yogyakarta Fak. Seni Rupa, ISI Yogyakarta, no. 9–10, 2021.

M. Alisani, L. I. Lette, and S. Koroy, “Karakteristik Morfologi Pohon Cemara Laut (Casuarina equisetifolia).”

D. Christopher Muntuuntu, Nurlena, and R. Ratna Mulyanti Karsiwi, “INOVASI KUE KERING BERBAHAN DASAR BUAH JAMBU BIJI 2019 (Studi Kasus dalam Produk Kue Nastar) INOVATION OF COOKIES BASED ON GUAVA 2019 (Case Study of Nastar Cookies Product),” e-Proceeding Appl. Sci., vol. 5, no. 3, pp. 2773–2778, 2019.

Y. N. Nabuasa, J. I. Komputer, U. N. Cendana, C. Digital, and E. Histogram, “PENGOLAHAN CITRA DIGITAL PERBANDINGAN METODE HISTOGRAM EQUALIZATION DAN,” vol. 7, no. 1, pp. 87–95, 2019.

D. M. Cnn, M. Arsal, B. Agus, and D. Anggraini, “Jurnal Nasional Teknologi dan Sistem Informasi Face Recognition Untuk Akses Pegawai Bank Menggunakan Deep Learning,” vol. 01, pp. 55–63, 2020.

G. G. Giantika, A. Munanjar, and I. W. Utomo, “Pelatihan Penggunaan Smartphone untuk Melakukan Foto Produk dan Editing Foto sebagai Pembuatan Iklan Produk bagi Anggota RPTRA Payung Tunas Teratai Jakarta Timur,” vol. 2, no. 2, pp. 123–134, 2022.

I. Wulandari, H. Yasin, and T. Widiharih, “Klasifikasi Citra Digital Bumbu Dan Rempah Dengan Algoritma Convolutional Neural Network (Cnn),” J. Gaussian, vol. 9, no. 3, pp. 273–282, 2020, doi: 10.14710/j.gauss.v9i3.27416.

A. Riyandi, T. Widodo, S. Uyun, and U. Islam Negeri Sunan Kalijaga Yogyakarta, “Classification of Damaged Road Images Using the Convolutional Neural Network Method Klasifikasi Pada Citra Jalan Rusak Menggunakan Metode Convolutional Neural Network,” J. Inform. dan Teknol. Inf., vol. 19, no. 2, pp. 147–158, 2022, doi: 10.31515/telematika.v19i2.6460.




DOI: https://doi.org/10.31315/telematika.v20i2.8774

DOI (PDF): https://doi.org/10.31315/telematika.v20i2.8774.g5655

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright of :
TELEMATIKA: Jurnal Informatika dan Teknologi Informasi
ISSN 1829-667X (print); ISSN 2460-9021 (online)


Dipublikasi oleh
Jurusan Teknik Informatika, UPN Veteran Yogyakarta
Jl. Babarsari 2 Yogyakarta 55281 (Kampus Unit II)
Telp: +62 274 485786
email: jurnaltelematika@upnyk.ac.id

 

Jurnal Telematika sudah diindeks oleh beberapa lembaga berikut:
 

 

 

 

 

Status Kunjungan Jurnal Telematika
slot gacor slot gacor hari ini slot gacor 2025 demo slot pg slot gacor slot gacor