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Optimasi SVM menggunakan algoritme grid search untuk identifikasi citra biji kopi robusta berdasarkan circularity dan eccentricity

SVM optimization using a grid search algorithm to identify robusta coffee bean images based on circularity and eccentricity

Department of Informatics, Universitas Singaperbangsa Karawang. Jl. HS.Ronggo Waluyo, Puseurjaya, Kec. Telukjambe Timur, Kabupaten Karawang, Jawa Barat 41361, Indonesia

Received: 26 Jun 2020; Revised: 10 Aug 2021; Accepted: 4 Jan 2022; Published: 31 Jan 2022.
Open Access Copyright (c) 2022 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Coffee variety is one of the main factors affecting the quality and price of coffee, so it is important to recognize coffee varieties. This study aims to optimize the recognition of robusta coffee beans based on circularity and eccentricity image features using a support vector machine (SVM) and Grid search algorithm. The methods used included image acquisition, preprocessing, feature extraction, classification, and evaluation. Circularity and eccentricity are used in the feature extraction process, while the grid search algorithm is used to optimize SVM parameters in the classification process for four different kernels. This study produced the best classification model with the highest accuracy of 94% for the RBF and Polynomial kernels.
Keywords: offee bean identification; grid search; parameter optimization; support vector machine
Funding: Universitas Singaperbangsa Karawang

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