MODEL HYBRID CNN-LSTM UNTUK DETEKSI KEBUTUHAN AIR BERDASARKAN KONDISI DAUN PADA CABAI MERAH LAJU F1
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Awalia, Ayu Rizkyca
Haris, Abdul
Siregar,S, Riki Ruli A.
Haris, Abdul
Siregar,S, Riki Ruli A.
Subject
Teknik Informatika
Datestamp
2023-06-26 08:10:46
Abstract :
Image data processing is generally done using the Convolutional Neural Network method. The use of CNN can be combined with other machine learning methods to improve the performance of the model being built, such as the CNN-LSTM hybrid. This study aims to find out how to apply and see the performance of the CNN-LSTM Hybrid model on the classification of chili plant water needs based on leaf imagery. The hybrid model was built using CNN as the feature extraction process and LSTM for the classification process. Extraction is carried out until the flatten stage, the flatten results are wrapped in TimeDistributed layers to be used as LSTM input and perform classification based on time series. In this study, classification was also carried out using CNN as a comparison of the performance of the hybrid model that was built. Accuracy results from model training give a value of 56% for the hybrid model and 63% for the CNN model. The results showed that the hybrid model on the water requirement classification of chili plants did not give maximum results compared to the CNN model without LSTM.
Image data processing is generally done using the Convolutional Neural Network method. The use of CNN can be combined with other machine learning methods to improve the performance of the model being built, such as the CNN-LSTM hybrid. This study aims to find out how to apply and see the performance of the CNN-LSTM Hybrid model on the classification of chili plant water needs based on leaf imagery. The hybrid model was built using CNN as the feature extraction process and LSTM for the classification process. Extraction is carried out until the flatten stage, the flatten results are wrapped in TimeDistributed layers to be used as LSTM input and perform classification based on time series. In this study, classification was also carried out using CNN as a comparison of the performance of the hybrid model that was built. Accuracy results from model training give a value of 56% for the hybrid model and 63% for the CNN model. The results showed that the hybrid model on the water requirement classification of chili plants did not give maximum results compared to the CNN model without LSTM.
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