Rekomendasi Pemilihan Jenis Tanaman Menggunakan Algoritma Random Forest dan XGBoost Regressor

Abdul Rahman; Daniel  Udjulawa; Mulyati Mulyati

doi:10.31294/coscience.v4i2.2987

Penulis

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

Kata Kunci:

Kata Kunci : Rekomendasi Tanaman, Machine Learning, Random Forest, XGBoost

Abstrak

Rekomendasi tanaman yang sesuai dengan kondisi lingkungan dan nutrisi tanah tempat tanaman ditanam dapat memberikan hasil panen yang optimal. Penerapan machine learning pada bidang pertanian telah banyak dilakukan terutama untuk meningkatkan hasil panen. Pada penelitian ini dua algoritma machine learning, yaitu Random forest dan XGBoost Regressor di implementasikan untuk merekomendasikan tanaman yang sesuai dengan kondisi linkungan dan nutrisi tanah. Umplementasi dari kedua algoritma tersebut dibandingkan tingkat akurasi menggunakan tiga alat ukur akurasi, yaitu Mean Absolute Error(MAE), Mean Square Error(MSE), dan R². Hasil yang didapat menunjukkan kedua algoritma mempunyai tingkat akurasi yang tidak jauh berbeda. Algoritma random Forest mempunyai tingkat akurasi yang lebih baik menggunakan MAE dan MSE, yaitu sebesar 36.73681574 dan 1.848396760, sedangkan algoritma XGBoost Regressor mempunyai tingkat akurasi yang baik dengan menggunakan alat ukur akurasi R² atau R-Square dengan tingkat akurasi sebesar 0.98542963509705.

Referensi

Banerjee, G., Sarkar, U., & Ghosh, I. (2021). A Fuzzy Logic-Based Crop Recommendation System (D. Bhattacharjee, D. K. Kole, N. Dey, S. Basu, & D. Plewczynski (ed.); hal. 57–69). Springer Singapore. https://doi.org/10.1007/978-981-15-7834-2_6

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/PEERJ-CS.623

Craker, L. E. (2021). 4 Plant Growth Factors That Affect All Plants. Journal of Soil Science & Plant Health, 5(3).

Filippi, P., Jones, E. J., Wimalathunge, N. S., Somarathna, P. D. S. N., Pozza, L. E., Ugbaje, S. U., Jephcott, T. G., Paterson, S. E., Whelan, B. M., & Bishop, T. F. A. (2019). An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precision Agriculture, 20(5), 1015–1029. https://doi.org/10.1007/s11119-018-09628-4

Johansen, K., Morton, M. J. L., Malbeteau, Y., Aragon, B., Al-Mashharawi, S., Ziliani, M. G., Angel, Y., Fiene, G., Negrão, S., Mousa, M. A. A., Tester, M. A., & McCabe, M. F. (2020). Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest. Frontiers in Artificial Intelligence, 3(May). https://doi.org/10.3389/frai.2020.00028

Li, Y., Zeng, H., Zhang, M., Wu, B., Zhao, Y., Yao, X., Cheng, T., Qin, X., & Wu, F. (2023). A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering. International Journal of Applied Earth Observation and Geoinformation, 118, 103269. https://doi.org/https://doi.org/10.1016/j.jag.2023.103269

Marques Ramos, A. P., Prado Osco, L., Elis Garcia Furuya, D., Nunes Gonçalves, W., Cordeiro Santana, D., Pereira Ribeiro Teodoro, L., Antonio da Silva Junior, C., Fernando Capristo-Silva, G., Li, J., Henrique Rojo Baio, F., Marcato Junior, J., Eduardo Teodoro, P., & Pistori, H. (2020). A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Computers and Electronics in Agriculture, 178, 105791. https://doi.org/https://doi.org/10.1016/j.compag.2020.105791

Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021). Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences, 1, 100010. https://doi.org/https://doi.org/10.1016/j.ailsci.2021.100010

Murugan, A., Nair, S. A. H., & Kumar, K. P. S. (2019). Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers. Journal of Medical Systems, 43(8), 269. https://doi.org/10.1007/s10916-019-1400-8

Pande, S M, Ramesh, P. K., Anmol, A., Aishwarya, B. R., Rohoilla, K., & Shaurya, K. (2021). Crop Recommender System Using Machine Learning Approach. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 1066–1071. https://doi.org/10.1109/ICCMC51019.2021.9418351

Pande, Shilpa Mangesh, Ramesh, P. K., Anmol, A., Aishwarya, B. R., Rohilla, K., & Shaurya, K. (2021). Crop Recommender System Using Machine Learning Approach. Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, Iccmc, 1066–1071. https://doi.org/10.1109/ICCMC51019.2021.9418351

Ravi, R., & Baranidharan, B. (2020). Crop Yield Prediction using XG Boost Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(5), 3516–3520. https://doi.org/10.35940/ijrte.d9547.018520

Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine Learning Applications for Precision Agriculture: A Comprehensive Review. IEEE Access, 9, 4843–4873. https://doi.org/10.1109/ACCESS.2020.3048415

Singh, R. (2023). Crop recommendation data (V1 ed.). Harvard Dataverse. https://doi.org/doi:10.7910/DVN/4GBWFV

Supriya, B. N., & Akki, C. B. (2021). Sentiment prediction using enhanced xgboost and tailored random forest. International Journal of Computing and Digital Systems, 10(1), 191–199. https://doi.org/10.12785/ijcds/100119

Upadhyay, H., Juneja, A., Turabieh, H., Malik, S., Gupta, A., Bitsue, Z. K., & Upadhyay, C. (2022). Exploration of Crucial Factors Involved in Plants Development Using the Fuzzy AHP Method. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/4279694

Wang, Y., Zhu, D., & Ding, Y. (2023). Identification of Winter Wheat- Identification of Winter Wheat-Growing Areas Based on the XGBoost Algorithm. Preprints. https://doi.org/10.20944/preprints202303.0346.v1

Xu, Q., & Yin, J. (2021). Application of Random Forest Algorithm in Physical Education. Scientific Programming, 2021, 1996904. https://doi.org/10.1155/2021/1996904

Xu, X., Gao, P., Zhu, X., Guo, W., Ding, J., Li, C., Zhu, M., & Wu, X. (2019). Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China. Ecological Indicators, 101, 943–953. https://doi.org/https://doi.org/10.1016/j.ecolind.2019.01.059