KLASIFIKASI DETEKSI NUTRISI PADA DAUN PADI MENGGUNAKAN METODE PRINCIPAL COMPONENT ANALYSIS DAN ALGORITMA EIGEN
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Institusion

Institut Teknologi Perusahaan Listrik Negara

Author

Nariska, Indah Septi
Siregar, Riki Ruli A.
Haris, abdul

Subject

Teknik Informatika 

Datestamp

2023-05-30 03:47:29 

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. 

File :
201831099_Indah Septi_Revisi Skripsi .pdf