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Daniel Udjulawa

Universitas Multi Data Palembang

Author-ID : 4240614

Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Other

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Rekomendasi Pemilihan Jenis Tanaman Menggunakan Algoritma Random Forest dan XGBoost Regressor Rahman, Abdul; Udjulawa, Daniel; Mulyati, Mulyati
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.2987

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

Rekomendasi Pemilihan Jenis Tanaman Menggunakan Algoritma Random Forest dan XGBoost Regressor Rahman, Abdul; Udjulawa, Daniel; Mulyati, Mulyati
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.2987

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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Journal : Computer Science (CO-SCIENCE)