https://fourier.or.id/index.php/FOURIER <p>Jurnal Fourier is a Scientific Journal that integrates and develops the science of Mathematics and its learning which is integrated and interconnected with Islamic values published since 2012 with the frequency of published 2 times a year with the main language (Indonesian and English) which the reviewer process according to the discipline (Analysis, Algebra, Applied Mathematics, Statistics, and Mathematics Education).</p> Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta en-US Jurnal Fourier 2252-763X https://fourier.or.id/index.php/FOURIER/article/view/237 <p style="font-weight: 400;"><strong>Let </strong><strong>&nbsp;be a graph. A labeling graph is a maps function of the set of vertices and/or edges of </strong><strong>, to the set of positive integers. A total modular labeling is said to be a </strong><strong>-modular total irregular labeling of the vertices of </strong><strong>, if for every two distinct vertices </strong><strong>&nbsp;and </strong><strong>&nbsp;in </strong><strong>, the modular weights are different, and belong to the set of integers </strong><strong>. The minimum </strong><strong>&nbsp;such that the graph </strong><strong>&nbsp;has a </strong><strong>- modular total irregular labeling is called the modular total vertex irregularity strength and denoted by </strong><strong>. In this paper, we study about the modular total vertex irregularity strength for the generalized Petersen graph </strong><em><strong>. </strong></em><em><strong>The result show that the exact value is</strong></em> <strong><em>.</em></strong></p> Dina Khairani Nasution Susilawati Copyright (c) 2025 Dina, Susilawati http://creativecommons.org/licenses/by-nc-sa/4.0 2025-04-30 2025-04-30 14 1 1 8 10.14421/fourier.2025.141.1-8 https://fourier.or.id/index.php/FOURIER/article/view/214 <p><strong>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. </strong></p> Intan Karunia Septiani Septiani Wika Dianita Utami Nurissaidah Ulinnuha Dino Ramadhan Copyright (c) 2025 Intan Karunia Septiani Septiani, Wika Dianita Utami, Nurissaidah Ulinnuha, Dino Ramadhan http://creativecommons.org/licenses/by-nc-sa/4.0 2025-04-30 2025-04-30 14 1 9 20 10.14421/fourier.2025.141.9-20