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

Main Article Content

Irzal Ahmad Sabilla Chastine Fatichah

Abstract

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.

Article Details

How to Cite
SABILLA, Irzal Ahmad; FATICHAH, Chastine. The Tomatoes and Chilies Type Classifications by Using Machine Learning Methods. Journal of Development Research, [S.l.], v. 4, n. 1, p. 1-6, may 2020. ISSN 2579-9347. Available at: <http://journal.unublitar.ac.id/jdr/index.php/jdr/article/view/93>. Date accessed: 19 sep. 2020. doi: https://doi.org/10.28926/jdr.v4i1.93.
Section
Engineering and Technology