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Saputra, Tommy
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Journal : Journal of Information Systems and Technology Research

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Learning Rate and Epoch Analysis for Medicinal Plant Identification Using GLCM and BPNN Nurqolbiah, fatihani; Absharina, Eriene Dheanda; Utari, Aspirani; Febriady, Mukhlis; Saputra, Tommy
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1413

Abstract

Accurate identification of medicinal plants is essential for pharmacology and biodiversity conservation. However, traditional methods rely heavily on subjective visual inspection, which is prone to misclassification due to subtle differences in leaf textures. A primary challenge that remains unaddressed is the understanding of hyperparameter sensitivity within limited datasets, particularly when the subjects exhibit extremely high visual similarity. This study proposes an automated identification approach using Gray-Level Co-occurrence Matrix (GLCM) and Backpropagation Neural Network (BPNN) to classify three Indonesian medicinal species: white ginger, mango ginger, and yellow turmeric. The distinctive focus of this research lies in its attempt to differentiate these specific plants, which possess leaf texture characteristics so similar that they are often indistinguishable to the human eye. This approach involves a systematic analysis of learning rate and epoch parameters to optimize convergence for these nearly identical texture features. A dataset of 63 images was transformed into five GLCM statistical features to serve as the primary inputs for the BPNN. Experimental results demonstrate that classification performance is highly sensitive to parameter tuning. The system achieved its peak accuracy of 65.03% using a learning rate of 0.1 and 100 epochs. The findings reveal that smaller learning rates and limited training iterations facilitate more stable convergence when processing data with high feature similarity. While the accuracy indicates potential for further development, this study provides a significant contribution to creating objective identification methods for visually similar plants and offers empirical insights into optimal parameter selection for texture-based neural network architectures.
Co-Authors Absharina, Eriene Dheanda Anita Desiani Armansyah Armansyah Darmawahyuni, Annisa Dian Palupi Rini Erwin Erwin Fachry Abda El Rahman Fauzan, Haidil Febriady, Mukhlis Haslindah, Andi Hernando, Luki Hidayat, Hendri Idawati Idawati M. Ansyar Bora Marchiano, Herli Muhammad Naufal Rachmatullah Muhammad Taufik Roseno, Muhammad Taufik Nurqolbiah, Fatihani Paulina Suri, Ghea Primanita, Anggina Putri Mirani Ririt Dwiputri Permatasari Salsabila, Tia Andini Sanusi Sapitri, Ade Iriani Shilah, Nur Sinta Bella Agustina Siti Nurmaini Sofian, Hocky Syaputra, Hadi Teisnajaya, Usep Twenty Agustine, Grace Ubaidillah Ubaidillah Utari, Aspirani Yudha Pratomo Yuritanto, Yuritanto