Record Details

The Tomatoes and Chilies Type Classifications by Using Machine Learning Methods: Classifications using Machine Learning Methods

Journal of Development Research

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Field Value
 
Title The Tomatoes and Chilies Type Classifications by Using Machine Learning Methods: Classifications using Machine Learning Methods
 
Creator Sabilla, Irzal Ahmad
Fatichah, Chastine
 
Subject classification
image processing
machine learning
 
Description Vegetables are ingredients for flavoring, such as tomatoes and chilies. A Both of these ingredients are processed to accompany the people's staple food in the form of sauce and seasoning. In supermarkets, these vegetables can be found easily, but many people do not understand how to choose the type and quality of chilies and tomatoes. This study discusses the classification of types of cayenne, curly, green, red chilies, and tomatoes with good and bad conditions using machine learning and contrast enhancement techniques. The machine learning methods used are Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), and Random Forest (RF). The results of testing the best method are measured based on the value of accuracy. In addition to the accuracy of this study, it also measures the speed of computation so that the methods used are efficient.
 
Publisher Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar
 
Date 2020-05-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier https://journal.unublitar.ac.id/jdr/index.php/jdr/article/view/93
10.28926/jdr.v4i1.93
 
Source Journal of Development Research; Vol. 4 No. 1 (2020): Volume 4, Number 1, May 2020; 1-6
2579-9347
2579-9290
 
Language eng
 
Relation https://journal.unublitar.ac.id/jdr/index.php/jdr/article/view/93/54
 
Rights Copyright (c) 2020 Journal of Development Research
http://creativecommons.org/licenses/by-sa/4.0