Rekomendasi Pemilihan Jenis Tanaman Menggunakan Algoritma Random Forest dan XGBoost Regressor

Abdul Rahman; Daniel  Udjulawa; Mulyati Mulyati

doi:10.31294/coscience.v4i2.2987

Authors

Abdul Rahman Universitas Multi Data Palembang
Daniel Udjulawa Universitas Multi Data Palembang
Mulyati Mulyati Universitas Multi Data Palembang

DOI:

https://doi.org/10.31294/coscience.v4i2.2987

Keywords:

Machine Learning, Random Forest, Crop Recommendation, XGBoost

Abstract

Recommendations for plants that suit a particular planting location's environmental conditions and soil nutrients can lead to optimal harvest outcomes. Machine learning applications in agriculture have been widely explored, particularly in enhancing crop yields. In this study, two machine learning algorithms, Random Forest and XGBoost Regressor, were implemented to recommend plants based on environmental conditions and soil nutrient levels. The implementation of both algorithms was compared in terms of accuracy using three accuracy metrics: Mean Absolute Error (MAE), Mean Square Error (MSE), and R2. The results indicated that both algorithms exhibited comparable accuracy levels. The Random Forest algorithm demonstrated superior accuracy in terms of MAE and MSE, with values of 36.73681574 and 1.848396760, respectively. Meanwhile, the XGBoost Regressor algorithm displayed good accuracy, mainly when measured using the R2 accuracy metric, achieving a high accuracy level of 0.98542963509705..

Keywords : Crop Recommendation, Machine Learning, Random Forest, XGBoost

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