Literatur Review: Penerapan Random Forest untuk Klasifikasi Penyakit Tanaman Padi

Anang Muhamad Lutfi; Eko Purwadi; Kamaluddin; Yusuf Ali Hanaan; Perani Rosyani

Authors

Anang Muhamad Lutfi Universitas Pamulang
Eko Purwadi Universitas Pamulang
Kamaluddin Universitas Pamulang
Yusuf Ali Hanaan Universitas Pamulang
Perani Rosyani Universitas Pamulang

Keywords:

Random Forest, Disease Classification, Rice Plants, Machine Learning, Indonesia

Abstract

Indonesia is an agrarian country where the agricultural sector plays a vital role in the economy. Diseases in rice plants pose a serious threat to farmers as they can significantly reduce the quality and yield of the harvest. Random Forest, one of the machine learning methods, has been implemented in research to effectively classify types of diseases in rice plants. This study reviews various literatures related to the application of the Random Forest method and several other algorithms such as CNN, Decision Tree, and SVM in detecting and identifying rice plant diseases. The review shows that the Random Forest method has high accuracy performance, making it a recommended method for early detection of rice plant diseases. This study is expected to serve as a guide for further research to improve the accuracy and efficiency of rice disease classification methods.

References

Agustiani, S., Tajul Arifin, Y., Junaidi, A., Khotimatul Wildah, S., & Mustopa, A. (n.d.). Klasifikasi Penyakit Daun Padi menggunakan Random Forest dan Color Histogram 1,*) (Vol. 10, Issue 1). https://www.kaggle.com/vbookshelf/rice-leaf-

Auza, H., Bagus Arisila Putra, M., Azril Saputra, M., Hartono, R., & Rosyani, P. (2024). Implementasi Deep Learning untuk Deteksi Wajah dan Ekspresi menggunakan Algoritma Convolutional Neural Network (CNN) dengan OpenCV. In Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan (Vol. 1, Issue 4). https://jurnalmahasiswa.com/index.php/ aidanspk

Habib Hawari, F., Fadillah, F., Rifqi Alviandi, M., & Arifin, T. (2022). KLASIFIKASI PENYAKIT PADI MENGGUNAKAN ALGORITMA CNN (CONVOLUTIONAL NEURAL NETWORK). JURNAL RESPONSIF, 4(2), 184–189. https://ejurnal.ars.ac.id/index.php/jti

Julianto, A., Sunyoto, A., Ferry, D., & Wibowo, W. (n.d.). OPTIMASI HYPERPARAMETER CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT TANAMAN PADI (OPTIMIZATION OF CONVOLUTIONAL NEURAL NETWORK HYPERPARAMETERS FOR CLASSIFICATION OF RICE PLANT DISEASES). https://www.kaggle.com/tedisetiady/leaf-rice-dis-

Purnamawati, A., Nugroho, W., Putri, D., & Hidayat, W. F. (2020). Deteksi Penyakit Daun pada Tanaman Padi Menggunakan Algoritma Decision Tree, Random Forest, Naïve Bayes, SVM dan KNN. InfoTekJar: Jurnal Nasional Informatika dan Teknologi Jaringan Attribution

NonCommercial 4.0 International. Some rights reserved 5(1). https://doi.org/10.30743/ infotekjar.v5i1.2934

Rosyani, P., Saprudin, S., & Amalia, R. (2021). Klasifikasi Citra Menggunakan Metode Random Forest dan Sequential Minimal Optimization (SMO). Jurnal Sistem Dan Teknologi Informasi (Justin), 9(2), 132. https://doi.org/10.26418/justin.v9i2.44120

Rozi, K., & Badri Tamam, M. (2024). KLASIFIKASI PENYAKIT TANAMAN PADI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DAN RANDOM FOREST (Vol. 7, Issue 2).