The Classification of Cropping Patterns Based on Regional Climate Classification Using Decision Tree Approach
T.A. Munandar, Sumiati

TL;DR
This paper develops a decision tree-based method to classify cropping patterns based on rainfall data, addressing climate change impacts on agricultural cropping schedules.
Contribution
It introduces a decision tree approach, specifically J48, for classifying cropping patterns using rainfall data, improving over traditional methods like Oldeman's.
Findings
J48 achieved higher classification accuracy than other algorithms.
Most areas in DKI Jakarta favor a 1 paddy + 1 CGPRT cropping pattern.
Banten has three recommended cropping patterns.
Abstract
Nowadays, agricultural field is experiencing problems related to climate change that result in the changing patterns in cropping season, especially for paddy and coarse grains, pulses roots and Tuber (CGPRT/Palawija) crops. The cropping patterns of rice and CGPRT crops highly depend on the availability of rainfall throughout the year. The changing and shifting of the rainy season result in the changing cropping seasons. It is important to find out the cropping patterns of paddy and CGPRT crops based on monthly rainfall pattern in every area. The Oldeman's method which is usually used in the classification of of cropping patterns of paddy and CGPRT crops is considered less able to determine the cropping patterns because it requires to see the rainfall data throughout the year. This research proposes an alternative solution to determine the cropping pattern of paddy and CGPRT crops based…
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