Classification of Land Suitability For Soybean Crops Using The Cart Method and Feature Selection Using an Algorithm ABC

Rusdi Efendi; Mochammad  Yusa; Stefani Tasya Hallatu

doi:10.25299/itjrd.2024.13595

Authors

Rusdi Efendi Program Studi Informatika, Fakultas Teknik, Universitas Bengkulu
Mochammad Yusa Universitas Bengkulu
Stefani Tasya Hallatu Universitas Bengkulu

DOI:

https://doi.org/10.25299/itjrd.2024.13595

Keywords:

Soyabean, Classification, CART, Feature selection, ABC

Abstract

The allocated area for soybean cultivation has been gradually decreasing, leading to a decline in both production and productivity. Consequently, the current level of soybean production and productivity falls short of meeting the demand within the community. One potential solution to augment soybean output and efficiency involves allocating specific parcels of land for soybean cultivation. It is essential to conduct land evaluations tailored to soybean cultivation, accounting for the land's inherent potential, in order to optimize land utilization. Thus, a comprehensive system is required to assess land suitability, particularly for soybean cultivation, and employ the results of this classification as recommendations for land allocation. This research employess combination the Classification and Regression Tree (CART) method and the Artificial Bee Colony (ABC) algorithm to classify suitable land for soybean cultivation. CART is used for classification and ABC is utilized for feature selection to identify the most relevant attributes in case of the algorithm improvement. Through a series of iterative experiments involving 5, 10, 25, 50, 75, and 100 iterations, the best attribute was determined following three attempts at each iteration. The Confusion Matrix test yielded an accuracy rate of 94.22% for the CART method in the second experiment, while the combined use of the best ABC and CART combination resulted in an accuracy rate of 97.11%. Therefore, it can be concluded that the integration of the artificial bee colony (ABC) algorithm with the classification and regression tree (CART) method outperforms the sole use of the CART method in terms of accuracy.

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