IMAGE CLASSIFICATION OF LOCAL ROBUSTA AND ARABICA COFFEE SEEDS IN MALANG REGENCY USING GRAY LEVEL CO-OCCURRENCE MATRIX AND K-NEAREST METHODS
| Autor: | Devita Widiawati, Muhammad Rijalun Shodaqu, Gilang Priambodo, Maulana Fajar Anas, Titien Suhartini Sukamto, Aris Nurhindarto |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Applied Intelligent System. 7:293-300 |
| ISSN: | 2502-9401 2503-0493 |
| DOI: | 10.33633/jais.v7i3.7214 |
| Popis: | Coffee is one type results current plantation this favored by some among. Indonesia is in the order to four Becomes Robusta coffee export and producer in the world. Appearance communities coffee lovers make coffee as provider field profession for part big resident. In Indonesia, especially in the Regency of Trunk, a lot very Public around who has coffee plantations including namely Robusta coffee and Arabica coffee (coffea arabica) local. For some new people Do you know and love coffee yet? can differentiate type of coffee visually. In the era of increasingly digitalization, advanced like this. There are several method for differentiate something object among them that is processing digital image. Frequent problems occur that is many less consumers in determine Robusta and Arabica coffee types. From trouble that, then researcher designing a system classification on robusta and Arabica coffee beans could obtained with implementation algorithm K-Nearest Lightweight Classification (K-NN). [1] combined with extraction feature Gray Level Co-Occurrence Matrix (GLCM). Digital image dataset used that is a total of 194 pictures where inside it there is type image coffee beans. Image dataset Robusta and Arabica coffee beans each local number of 97 images. Image dataset shared into 20 test data and 174 training data. Testing conducted using Matlab software produce score accuracy highest at distance pixels=1 and the value of K=1 with respect to angle of 45° by 95%. |
| Databáze: | OpenAIRE |
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