TY - JOUR
T1 - Discrimination of durian ripeness level using gas sensors and neural network
AU - Rivai, Muhammad
AU - Budiman, Fajar
AU - Purwanto, Djoko
AU - Al Baid, Mohammad Syahrian Adil
AU - Tukadi,
AU - Aulia, Dava
N1 - Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V.
PY - 2021
Y1 - 2021
N2 - In the agriculture industry, determining the ripeness level of fruits is a very important aspect. This is related to maintaining the quality of the production, and during the distribution process. Currently, human sensory tests are still commonly used to evaluate food products with inconsistent results. This study developed a system to discriminate the durian ripeness level using gas sensors and neural network based on the character of the fruit aroma. This system succeeded in distinguishing the ripeness of durian including unripe, ripe and overripe with performance evaluation values above 91%.
AB - In the agriculture industry, determining the ripeness level of fruits is a very important aspect. This is related to maintaining the quality of the production, and during the distribution process. Currently, human sensory tests are still commonly used to evaluate food products with inconsistent results. This study developed a system to discriminate the durian ripeness level using gas sensors and neural network based on the character of the fruit aroma. This system succeeded in distinguishing the ripeness of durian including unripe, ripe and overripe with performance evaluation values above 91%.
KW - Agriculture
KW - Durian ripeness level
KW - Food
KW - Gas sensors
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=85123754862&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2021.12.188
DO - 10.1016/j.procs.2021.12.188
M3 - Conference article
AN - SCOPUS:85123754862
SN - 1877-0509
VL - 197
SP - 677
EP - 684
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 6th Information Systems International Conference, ISICO 2021
Y2 - 7 August 2021 through 8 August 2021
ER -