Please use this identifier to cite or link to this item: http://repository.unmul.ac.id/handle/123456789/10237
Title: Diagnose Pest and Disease of Black Orchid Plant Using Naive Bayes Method
Authors: Gifari, Okta Ihza
Agus, Fahrul
Ramadiani, Ramadiani
Azhari, Muhamad
Sunyoto, Andi
Keywords: Expert Systems
Bayes Theorem Method
Black Orchid
Pests Plant Disease
Mean Absolute Percentage Error (MAPE)
Issue Date: 6-Oct-2021
Publisher: 2021 IEEE 7th Information Technology International Seminar (ITIS) Surabaya, Indonesia, October 6-8, 2021
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%.
URI: http://repository.unmul.ac.id/handle/123456789/10237
ISBN: ISBN: 978-1-6654-0807-3
Appears in Collections:P - Computer Sciences and Information Technology

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