Discrimination of durian ripeness level using gas sensors and neural network

Muhammad Rivai; Fajar Budiman; Djoko Purwanto; Mohammad Syahrian Adil Al Baid; Tukadi; Dava Aulia

doi:10.1016/j.procs.2021.12.188

Discrimination of durian ripeness level using gas sensors and neural network

Muhammad Rivai
, Fajar Budiman
, Djoko Purwanto
, Mohammad Syahrian Adil Al Baid
, Tukadi
, Dava Aulia

Institut Teknologi Sepuluh Nopember

Research output: Contribution to journal › Conference article › peer-review

28 Scopus citations

Abstract

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%.

Original language	English
Pages (from-to)	677-684
Number of pages	8
Journal	Procedia Computer Science
Volume	197
DOIs	https://doi.org/10.1016/j.procs.2021.12.188
State	Published - 2021
Externally published	Yes
Event	6th Information Systems International Conference, ISICO 2021 - Virtual, Online, Italy Duration: 7 Aug 2021 → 8 Aug 2021

Keywords

Agriculture
Durian ripeness level
Food
Gas sensors
Neural network

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10.1016/j.procs.2021.12.188

Cite this

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title = "Discrimination of durian ripeness level using gas sensors and neural network",

abstract = "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\%.",

keywords = "Agriculture, Durian ripeness level, Food, Gas sensors, Neural network",

author = "Muhammad Rivai and Fajar Budiman and Djoko Purwanto and \{Al Baid\}, \{Mohammad Syahrian Adil\} and Tukadi and Dava Aulia",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors. Published by Elsevier B.V.; 6th Information Systems International Conference, ISICO 2021 ; Conference date: 07-08-2021 Through 08-08-2021",

year = "2021",

doi = "10.1016/j.procs.2021.12.188",

language = "English",

volume = "197",

pages = "677--684",

journal = "Procedia Computer Science",

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publisher = "Elsevier BV",

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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

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 - https://www.scopus.com/pages/publications/85123754862

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 -

Discrimination of durian ripeness level using gas sensors and neural network

Abstract

Keywords

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