@thesis{thesis,
		author={Haris  abdul and Nariska  Indah Septi and Siregar  Riki Ruli A.},
		title ={KLASIFIKASI DETEKSI NUTRISI PADA DAUN PADI MENGGUNAKAN METODE PRINCIPAL COMPONENT ANALYSIS DAN ALGORITMA EIGEN},
		year={2023},
		url={http://156.67.221.169/5660/},
		abstract={In this study, the detection of nutrients in the leaves of rice plants was carried out using the
Principal Component Analysis (PCA) method and the Eigen Algorithm. In this study the 
dataset used came from the Kaggle platform, totaling 1156. The rice leaf images were 
divided into 3 classes, namely Nitrogen (N), Phosphorus (P), Potassium (K) which were
tested with a ratio of 60% train data and 40% test data and validity. From the results of 
this study obtained an accuracy of 47.89%, a precision of 46.7%, a recall of 45.9%, and 
an F1-Score of 46.2% with an image size of 128x128. The results of accuracy, precision, 
recall, and F1-Score using the Principal Component Analysis (PCA) model are still quite 
low, therefore for future research it is necessary to further explore the algorithm and the 
number of datasets used so that the results are better.}
		}