Agriculture is one of the important sectors in Indonesia. There are several commodities consumed by Indonesian citizens, one of them is shallots as a cooking spice and herbal medicine. Several problems occasionally arise in the shallots cultivation process; one of them is increasing plant pest organism’s attacks. However, the control process is still done conventionally. Therefore, it is necessary to build a system to diagnose diseases automatically. This paper proposes an information system for agriculture shallots based on machine learning using dempster Shafer. After collecting the data, dempster Shafer is applied to determine and define a reasonable level of confidence and a function to evaluate a possibility. Based on the experiment, the accuracyofmax belief for detection of shallots disease is 60%. By using several scenarios, the experimental result shows that dempster Shafer can detect shallot disease well. Furthermore, the algorithm can be implemented in web and mobile applications based to monitor the controlling process easier.
Skip Nav Destination
Article navigation
16 May 2023
THE 6TH INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT, EPIDEMIOLOGY AND INFORMATION SYSTEM (ICENIS) 2021: Topic of Energy, Environment, Epidemiology, and Information System
4–5 August 2021
Semarang, Indonesia
Research Article|
May 16 2023
Agriculture information system for horticulture based on machine learning Available to Purchase
A. Sumarudin;
A. Sumarudin
a)
Informatics Engineering, Politeknik Negeri Indramayu
, Indramayu 45253, Indonesia
a)Corresponding author: [email protected]
Search for other works by this author on:
Alifia Puspaningrum;
Alifia Puspaningrum
b)
Informatics Engineering, Politeknik Negeri Indramayu
, Indramayu 45253, Indonesia
Search for other works by this author on:
Adi Suheryadi;
Adi Suheryadi
c)
Informatics Engineering, Politeknik Negeri Indramayu
, Indramayu 45253, Indonesia
Search for other works by this author on:
Harsa Yamani
Harsa Yamani
d)
Informatics Engineering, Politeknik Negeri Indramayu
, Indramayu 45253, Indonesia
Search for other works by this author on:
A. Sumarudin
a)
Informatics Engineering, Politeknik Negeri Indramayu
, Indramayu 45253, Indonesia
Alifia Puspaningrum
b)
Informatics Engineering, Politeknik Negeri Indramayu
, Indramayu 45253, Indonesia
Adi Suheryadi
c)
Informatics Engineering, Politeknik Negeri Indramayu
, Indramayu 45253, Indonesia
Harsa Yamani
d)
Informatics Engineering, Politeknik Negeri Indramayu
, Indramayu 45253, Indonesia
a)Corresponding author: [email protected]
AIP Conf. Proc. 2683, 050013 (2023)
Citation
A. Sumarudin, Alifia Puspaningrum, Adi Suheryadi, Harsa Yamani; Agriculture information system for horticulture based on machine learning. AIP Conf. Proc. 16 May 2023; 2683 (1): 050013. https://doi.org/10.1063/5.0139782
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
23
Views
Citing articles via
The implementation of reflective assessment using Gibbs’ reflective cycle in assessing students’ writing skill
Lala Nurlatifah, Pupung Purnawarman, et al.
Effect of coupling agent type on the self-cleaning and anti-reflective behaviour of advance nanocoating for PV panels application
Taha Tareq Mohammed, Hadia Kadhim Judran, et al.
Design of a 100 MW solar power plant on wetland in Bangladesh
Apu Kowsar, Sumon Chandra Debnath, et al.
Related Content
Implementation YOLOv3 for symptoms of disease in shallots crop
AIP Conf. Proc. (April 2023)
Identification of pineapple maturity utilizing digital image using hybrid machine learning method
AIP Conf. Proc. (April 2024)
Rainfall prediction using machine learning techniques
AIP Conf. Proc. (May 2024)
Development of bio-medicinal plants and herbs classifier with random forest algorithm and QR code generator
AIP Conf. Proc. (December 2024)
Genetic diversity analysis of F2 red chili (Capsicum annum L.) population of laris x SSP cross breeding based on SSR markers
AIP Conf. Proc. (December 2023)