Predicting the Planting Time of Bird's Eye Chili Based on Environmental Conditions Using Internet of Things (IoT) and Neural Network Method
Dublin Core
Title
Predicting the Planting Time of Bird's Eye Chili Based on Environmental Conditions Using Internet of Things (IoT) and Neural Network Method
Subject
Red Tabasco pepper, neural network, planting time, prediction
Description
In Indonesian cuisine, Red Tabasco pepper holds a significant place as a commonly used ingredient. However, the cultivation
of this chili variety is not without its challenges, primarily due to the volatile nature of chili prices. Farmers often grapple with
the critical decision of when to plant Tabasco pepper to optimize their yields and income. Understanding the complexities of
this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advancements in
Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study delves into the
development of an IoT-based solution aimed at assisting farmers in precisely determining the optimal planting time for Tabasco
pepper. It leverages five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight
(hours), and groundwater usage data (m3)—to make data-driven planting decisions. The pressing need for such a system
becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Utilizing
historical data from 2019, obtained from the DKI Jakarta Provincial Government Open Data, and climate data from the
Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based
prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The outcome is a reliable
prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the
ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market.
of this chili variety is not without its challenges, primarily due to the volatile nature of chili prices. Farmers often grapple with
the critical decision of when to plant Tabasco pepper to optimize their yields and income. Understanding the complexities of
this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advancements in
Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study delves into the
development of an IoT-based solution aimed at assisting farmers in precisely determining the optimal planting time for Tabasco
pepper. It leverages five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight
(hours), and groundwater usage data (m3)—to make data-driven planting decisions. The pressing need for such a system
becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Utilizing
historical data from 2019, obtained from the DKI Jakarta Provincial Government Open Data, and climate data from the
Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based
prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The outcome is a reliable
prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the
ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market.
Creator
Yan Mitha Djaksana, Agus Buono, Sri Wahjuni, Heru Sukoco
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
December 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Yan Mitha Djaksana, Agus Buono, Sri Wahjuni, Heru Sukoco, “Predicting the Planting Time of Bird's Eye Chili Based on Environmental Conditions Using Internet of Things (IoT) and Neural Network Method,” Repository Horizon University Indonesia, accessed April 25, 2026, https://repository.horizon.ac.id/items/show/10158.