Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Fourier
Dino Ramadhan
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Education Mathematics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Classification of Wood Types Based on Wood Fiber Texture Using GLCM - ANN Septiani, Intan Karunia Septiani; Wika Dianita Utami; Nurissaidah Ulinnuha; Dino Ramadhan
Jurnal Fourier Vol. 14 No. 1 (2025)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2025.141.9-20

Abstract

In Indonesia, various types of wood grow and develop with various characteristics and benefits. Each type of wood has differences in texture and fiber, to classify it must have sufficient knowledge about the texture and fiber of wood. A wood species identification system is needed to help the classification process. The purpose of this research is to classify Teak Wood, Sengon Wood, Mahogany Wood, and Gmelina Wood which are often sold in Indonesia. The classification method used in this research is Artificial Neural Network with Gray Level Co- occurrence Matrix (GLCM) extraction. Pre-processing stages include Histogram Equalization, filtering, converting images into grayscale form, and data augmentation. Feature extraction of pre-processing results using GLCM is taken, namely contrast, correlation, energy, homogeneity, and entropy. From the research results, classification using Artificial Neural Network was obtained with 46% accuracy, 43% precision, 42.5% recall, and 42% F1-Score with a GLCM inclination angle of 90°. So, this method can be used to classify the types of wood, but it is less accurate because there are still deficiencies in the model.
Co-Authors Nurissaidah Ulinnuha Septiani, Intan Karunia Septiani Wika Dianita Utami