| Issue |
MATEC Web Conf.
Volume 101, 2017
Sriwijaya International Conference on Engineering, Science and Technology (SICEST 2016)
|
|
|---|---|---|
| Article Number | 03012 | |
| Number of page(s) | 6 | |
| Section | Mechanical, Industrial and Manufacturing Engineering | |
| DOI | https://doi.org/10.1051/matecconf/201710103012 | |
| Published online | 09 March 2017 | |
A probability approach in cans identification
1 Department of Mathematics, Faculty of Mathematics and Natural Science Universitas Sriwijaya, 30162 Inderalaya, Indonesia
2 Department of Mechanical Engineering Faculty of Engineering Universitas Sriwijaya, 30162 Inderalaya, Indonesia
* Corresponding author: yulia_restit@mipa.unsri.ac.id
The objective of this study is to identify can waste into three types based on the images by using a probability approach of trinomial distribution in term regression. Predictor variables considered are the color intensity of red, green, and blue of the images taken at the top, down, and side pose successively. From an independence test between each of the predictor variable and can waste type noted that only the color intensity of red which the image taken at top pose that does not correspond to the can waste types. Based on the Nagelkerke value is found that the variance of the predictor variable data in identifying the can waste type is able to explain the variance of the types of 59.1 percent. The final model show that the significant predictor variables are the colors intensity of green and blue which the image taken at the top pose, the color intensity of red which the image taken at down pose, and the color intensity of red, green and blue which the image taken at side pose successively. The model can identify cans waste into three types based on the images correctly by 73.13%.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.