Open AccessDesign of System for Determining Corn Seed Color Quality Standards using the Fuzzy Logic Method through the Localhost Network
Author(s) -
Reksa Nirvana Alam
Publication year - 2019
Publication title -
jurnal jaringan telekomunikasi
Language(s) - English
Resource type - Journals
eISSN - 2654-6531
pISSN - 2407-0807
DOI - 10.33795/jartel.v8i1.167
Subject(s) - sorting , sieve (category theory) , artificial intelligence , fuzzy logic , color analysis , sieve analysis , computer vision , mathematics , computer science , engineering , algorithm , combinatorics , geotechnical engineering
Control of quality standards is very important role in ensuring corn on the market. Corn seed quality standards are determined from the results of classification process applied. So far, evaluation process of classification of corn seeds quality is still done manually which takes a long time and the quality of product is'nt evenly distributed. So, we need a tool to determine corn seeds quality to improve its quality. This study conducted color readings of corn seeds using TCS230 color sensors and sorting diameter of corn seeds using a small, medium, and large diameter sieve machine. The method for classifying quality standard of corn seed color uses fuzzy logic. The test was carried out by taking data from 3 TCS230 color sensors on each diameter of the sieve machine for corn seeds types used are BISI-2 and BIMA-19. The sensor accuracy is known by comparing data from sensor with data from Color Grab application. The reading results of BISI-2 on the color sensor-1 shows an accuracy rate of 0.3%, the color sensor-2 shows an accuracy rate of 0.72%, and the color sensor-3 shows an accuracy rate of 1.76%. For BIMA-19 corn seeds, the reading on color sensor-1 shows an accuracy of 1.11%, the color sensor-2 shows an accuracy of 24.6%, the color sensor-3 shows an accuracy of 1.10%. The results of fuzzy testing on BISI-2 and BIMA-19 showed that quality standard of maize seeds was good at medium and large diameters, while those on small diameters showed poor quality standards.