dc.contributor.author | Gifari, Okta Ihza | |
dc.contributor.author | Agus, Fahrul | |
dc.contributor.author | Ramadiani, Ramadiani | |
dc.contributor.author | Azhari, Muhamad | |
dc.contributor.author | Sunyoto, Andi | |
dc.date.accessioned | 2022-01-15T09:11:58Z | |
dc.date.available | 2022-01-15T09:11:58Z | |
dc.date.issued | 2021-10-06 | |
dc.identifier.isbn | ISBN: 978-1-6654-0807-3 | |
dc.identifier.other | IEEE Catalog Number: CFP21X12-ART | |
dc.identifier.uri | http://repository.unmul.ac.id/handle/123456789/10237 | |
dc.description.abstract | Abstract— Coelogyne pandurata or better known by
Kalimantan Black Orchid. This plant is an epiphytic orchid
that is attached to other plants but is not dangerous. This
orchid is one of Kalimantan’s endemic plants that require
human intervention to save its sustainability. Black Orchid
Plants are very susceptible to various pests and diseases;
therefore, applications are needed that can provide insight
to ornamental plant farmers. This study uses the Naïve
Bayes methods in the application of Expert Systems to
diagnose pests and diseases of Black Orchid plants. There
are 24 types of symptoms and nine types of diagnostic
results. The results of this study are expected to provide a
solution for cultivating Black Orchid plants so that they can
find out the symptoms of pests and plant diseases early on.
Nine diagnostic results consist of 4 types of pests and five
types of disease. This application has a Mean Absolute
Percentage Error (MAPE) value of 2.456%. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | 2021 IEEE 7th Information Technology International Seminar (ITIS) Surabaya, Indonesia, October 6-8, 2021 | en_US |
dc.subject | Expert Systems | en_US |
dc.subject | Bayes Theorem Method | en_US |
dc.subject | Black Orchid | en_US |
dc.subject | Pests Plant Disease | en_US |
dc.subject | Mean Absolute Percentage Error (MAPE) | en_US |
dc.title | Diagnose Pest and Disease of Black Orchid Plant Using Naive Bayes Method | en_US |
dc.type | Article | en_US |