PENERAPAN METODE SUPPORT VECTOR MACHINE (SVM) PADA KLASIFIKASI JENIS CENGKEH BERDASARKAN FITUR TEKSTUR DAUN

Jurnal Riset Sistem dan Teknologi Informasi Pub Date : 2024-02-02 DOI:10.30787/restia.v2i1.1364

Sadri Talib, Sakinah Sudin, Muhammad Dzikrullah Suratin

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Abstract

Leaves are a very important plant component because they play an important role in differentiating plant species, including clove plants. Currently, the identification of clove species, namely Afo, Siputih, and Zanzibar, relies on manual observation of the characteristics of the fruit and flowers, which can take a long time, especially considering the long fruiting period of the clove plant. To answer this problem, the authors conducted a study to classify the three types of clove leaves based on the characteristics and texture of the Gray gray-level co-occurrence Matrix (GLCM), which includes four parameters: Contrast, Correlation, Energy, and Homogeneity. The Support Vector Machine (SVM) classification algorithm processes extracted feature values and accurately class leaves. This study achieves the highest accuracy of 56.67% on an image size of 250x250 pixels and 48.33% on an image size of 150x150 pixels using 150 training data and 60 test data. These results indicate the potential of automatic leaf classification in efficiently identifying clove plant species. Keywords : Clove, Leaf, Processing, Texture, SVM

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基于叶片纹理特征的支持向量机（SVM）方法在丁香类型分类中的应用

叶片是一种非常重要的植物成分，因为它在区分包括丁香植物在内的植物物种方面发挥着重要作用。目前，丁香品种（即阿福、西普提和桑给巴尔）的鉴定主要依靠人工观察果实和花朵的特征，这可能需要很长时间，尤其是考虑到丁香植物的结果期较长。为了解决这个问题，作者进行了一项研究，根据灰度级共现矩阵（GLCM）的特征和纹理对三种丁香叶进行分类：对比度、相关性、能量和同质性。支持向量机（SVM）分类算法处理提取的特征值，并对树叶进行准确分类。这项研究使用 150 个训练数据和 60 个测试数据，在 250x250 像素大小的图像上取得了 56.67% 的最高准确率，在 150x150 像素大小的图像上取得了 48.33% 的最高准确率。这些结果表明了叶片自动分类在有效识别丁香植物种类方面的潜力。关键词：丁香叶片处理纹理 SVM

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