California Papaya Fruit Maturity Classification Uses Learning Vector Quantization
- DOI
- 10.2991/absr.k.210304.045How to use a DOI?
- Keywords
- Classification of maturity, Papaya California, Learning Vector Quantization
- Abstract
This research aims to build a system for the classification of papaya maturity level using Learning Vector Quantization. The classification process is done by the colour feature extraction value. Forty-five images consist of 30 images for training data and 15 images for test data were used. The images were divided into 3 classes: rip, mature and raw. The parameters for classification are mean, skewness, and kurtosis. Test results 1 obtained an accuracy of 60% consisting of 9 true images and 6 incorrect images with hidden layer 5 and learning rate 0,1. Test results 2 obtained an accuracy of 66,67% consisting of 10 true images and 5 incorrect images with hidden layer 10 and learning rate 0,5. Test image data are 15 papaya images consisting of 5 mature images, 5 imperfect images, and 5 raw images.
- Copyright
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Romi Wiryadinata AU - Andy A. Fatmawaty AU - Muhammad Saepudin AU - Alimuddin AU - Oktavia Widia Ningrum AU - Imamul Muttakin PY - 2021 DA - 2021/03/04 TI - California Papaya Fruit Maturity Classification Uses Learning Vector Quantization BT - Joint proceedings of the 2nd and the 3rd International Conference on Food Security Innovation (ICFSI 2018-2019) PB - Atlantis Press SP - 243 EP - 247 SN - 2468-5747 UR - https://doi.org/10.2991/absr.k.210304.045 DO - 10.2991/absr.k.210304.045 ID - Wiryadinata2021 ER -