@thesis{thesis,
		author={Kuswardani  Dwina and MIFTA  TEUKU ABRAR and Siregar  Riki Ruli Affandi},
		title ={PENERAPAN METODE SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI JENIS BIJI KOPI ROBUSTA DAN ARABICA},
		year={2020},
		url={http://156.67.221.169/4302/},
		abstract={Coffee beans are often mixed up nowadays due to the large demand for coffee beans globally, including Indonesia and broadly the types of Arabica and Robusta. Research on the detection of coffee beans that have been carried out using segmentation and development based on features and shapes with the Histogram of Oriented Gradient Method and classification using a Support Vector Machine, in detecting coffee beans by floating segmentation, it is very sensitive
to differences in image conditions such as illumination, noise, and recognition. In this study, feature extraction using the Histogram of Oriented Gradient method at the pixel level, this study was developed with 100 dataset images consisting of
50 Arabica datasets and 50 Robusta datasets, with classifiers using machine learning methods, namely, Support Vector Machine which is applied to per image area. The Support Vector Machine method is a binary classification method
wherein the problem of detecting coffee beans the Support Vector Machine can distinguish between Arabica and Robusta coffee beans and object locking using Sliding Windows. Also, to describe the object in this study, texture and shape
features are used, and the classification accuracy results are using the Confusion Matrix method manually. The results of this study found an accuracy of 58% of the correct prediction from the classification}
		}