Support Vector Machine Application for Classification of Tempe Fermentation Maturity with Information Gain Selection Feature

Muhammad Irfak, Istiadi, Aviv Yuniar Rahman
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Abstract

Tempe is one of the ingredients of traditional Indonesian cuisine. In making tempe, a soybean fermentation process is needed which is generally still carried out in an open environment so that the maturity time becomes slow and erratic. Therefore, in the tempe fermentation process, a detector is needed to find out optimal maturity in tempe. This detection effort makes it possible to use image processing by utilizing various feature extractions through the classification process. This research utilizes a variety of image features, namely texture features using the GLCM method and various color features, namely RGB, HSV, LAB, CMYK, YUV, HIS, HCL, LCH. However, with so many features, it causes a high computational load, so that in this study the Information Gain approach was used to select features. Furthermore, the classification process is carried out using the Support Vector Machine (SVM) method with variations of linear, polynomial, gaussian and sigmoid kernels. Tempe objects in the fermentation process are divided into unripe, ripe and rotten classes with a total of 410 images as a dataset. The test results (SVM+IG) on the Sigmoid kernel produce the fastest time accuracy with a computational result of 2.18 seconds on a 30:70 split ratio, the longest split ratio is 80:20 which is 2.50 seconds on a Linear kernel and produces the highest accuracy of 96 ,74%. Furthermore, in the SVM test without using Information Gain on the gaussian kernel, it produced the fastest time accuracy of 2.28 seconds, and the longest at a split ratio of 40:60, namely 3.00 seconds in the polynomial kernel. Thus the result of using SVM+IG is that the average level of accuracy when using (SVM+IG) is faster than the SVM process without IG which obtains slower computation time. Based on the description above, this study aims to apply the SVM method to classify tempe fermented images with feature selection using Information Gain.
基于信息增益选择特征的支持向量机在坦贝发酵成熟度分类中的应用
丹贝是印尼传统美食的原料之一。在制作豆豉时,需要大豆发酵过程,该过程通常仍在开放的环境中进行,因此成熟时间变得缓慢而不稳定。因此,在teme发酵过程中,需要一种检测器来确定teme的最佳成熟度。这种检测工作使得通过分类过程利用各种特征提取来使用图像处理成为可能。本研究利用了多种图像特征,即GLCM方法的纹理特征和各种颜色特征,即RGB、HSV、LAB、CMYK、YUV、HIS、HCL、LCH。然而,由于特征数量多,计算量大,因此本研究采用信息增益方法进行特征选择。在此基础上,采用支持向量机(SVM)方法,利用线性核、多项式核、高斯核和s型核进行分类。发酵过程中的Tempe对象分为未熟、熟、烂三类,共410张图像作为数据集。在Sigmoid内核上的测试结果(SVM+IG)在30:70的分割比下产生最快的时间精度,计算结果为2.18秒,在Linear内核上最长的分割比为80:20,计算结果为2.50秒,产生最高的准确率为96.74%。此外,在不使用高斯核信息增益的SVM测试中,其时间精度最快为2.28秒,在分割比为40:60时最长,即在多项式核中为3.00秒。因此,使用SVM+IG的结果是,使用(SVM+IG)时的平均精度水平比不使用IG的SVM过程快,但计算时间较慢。基于以上描述,本研究旨在利用信息增益的特征选择,应用SVM方法对发酵图像进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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