Identifikasi Daun Durian Dengan Computer Vision
Berdasarkan Fitur Bentuk
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Institusion
Politeknik Negeri Jember
Author
Addini, Wahidah
Subject
458 - Teknik Informatika
Datestamp
2021-11-03 04:45:24
Abstract :
Durian has the scientific name Durio Zibethinus, this fruit has the nickname "The King of Fruits". Durian is an annual fruit that grows in tropical climates, in Indonesia. Director General of Horticulture Suwandi (2019) said that durian export volume in 2018 rose to 353 percent compared to 2017. Exports of durian fruit were 700 tons higher than imports of durian fruit. With the existence of various types of durian seeds being bred, it is not surprising that farmers sometimes still have difficulty in distinguishing each type of durian seed. The purpose of this study was to determine the results of shape feature extraction to determine the type of leaves on durian seedlings and to determine the level of accuracy of the Random Forest method in identifying the types of durian leaves. Therefore, research will be conducted using the Random Forest method using shape feature extraction. The results of the classification using the Random Forest method are 93% with a total of 15 leaf data testing.
Durian has the scientific name Durio Zibethinus, this fruit has the nickname "The King of Fruits". Durian is an annual fruit that grows in tropical climates, in Indonesia. Director General of Horticulture Suwandi (2019) said that durian export volume in 2018 rose to 353 percent compared to 2017. Exports of durian fruit were 700 tons higher than imports of durian fruit. With the existence of various types of durian seeds being bred, it is not surprising that farmers sometimes still have difficulty in distinguishing each type of durian seed. The purpose of this study was to determine the results of shape feature extraction to determine the type of leaves on durian seedlings and to determine the level of accuracy of the Random Forest method in identifying the types of durian leaves. Therefore, research will be conducted using the Random Forest method using shape feature extraction. The results of the classification using the Random Forest method are 93% with a total of 15 leaf data testing.
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