KLASIFIKASI CITRA RUMPUT MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) BERDASARKAN WARNA
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Fahira, Nafa
Haris, Abdul
Yosrita, Efy
Haris, Abdul
Yosrita, Efy
Subject
Teknik Informatika
Datestamp
2022-09-26 07:28:39
Abstract :
From livestock statistical data, most of the livestock in Indonesia are smallholder farms. So that the source of feed is taken from natural grass that grows wild such as in gardens, roadsides, rice fields, fields, forest edges or agricultural or plantation products without paying attention to the quality of animal feed. So that many breeders during the dry season take natural grass arbitrarily which causes livestock to consume toxic natural grass which can interfere with physiological processes and make livestock production decrease. From the above problems, the authors take advantage of computational technology using Support Vector Machine (SVM) method in classifying natural grass based on the color of the grass. The grass classification process in this study is based on color characteristics, namely taking the value min, max and mean RGB from the image. Natural grass image data retrieval using a cellphone camera. The dataset used is 23 images of training data and 10 images of tesingt data. The results of this study indicate that grass classification using the SVM method produces an accuracy of 90% with confusion matrix as reference.
From livestock statistical data, most of the livestock in Indonesia are smallholder farms. So that the source of feed is taken from natural grass that grows wild such as in gardens, roadsides, rice fields, fields, forest edges or agricultural or plantation products without paying attention to the quality of animal feed. So that many breeders during the dry season take natural grass arbitrarily which causes livestock to consume toxic natural grass which can interfere with physiological processes and make livestock production decrease. From the above problems, the authors take advantage of computational technology using Support Vector Machine (SVM) method in classifying natural grass based on the color of the grass. The grass classification process in this study is based on color characteristics, namely taking the value min, max and mean RGB from the image. Natural grass image data retrieval using a cellphone camera. The dataset used is 23 images of training data and 10 images of tesingt data. The results of this study indicate that grass classification using the SVM method produces an accuracy of 90% with confusion matrix as reference.
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