@thesis{thesis,
		author={Nazalia  Cendekia Luthfieta and Palupiningsih  Pritasari and Prayitno  Budi},
		title ={IMPLEMENTASI ALGORITMA CONVOLUTIONAL NEURAL NETWORK UNTUK DETEKSI HAMA DI TANAMAN SAWI HIJAU},
		year={2022},
		url={http://156.67.221.169/5789/},
		abstract={High demand for caisim in Indonesia's main export commodity must be 
accompanied by a good planting process. The obstacle faced is that farmers are currently 
able to apply pesticides when the caisim plants have holes due to being eaten by pests.
This control can be a good step to maximize the yield of caisim farming. The high demand 
for caisim in Indonesia's main export commodity must be carried out with a good planting 
process. However, many farmers have not implemented proper control of pests, one of 
which is farmers in Kebon Raya Dempo. The obstacles faced such as not being able to 
detect pests correctly and provide pesticides with precision. This control can be a good 
step to maximize the yield of caisim farming. Motivated by CNN's success in image 
classification, a learning-based approach has been carried out in this study to detect the 
presence of pests in caisim. The experimental results show differences in accuracy in each 
experiment with a dataset of 1000, consisting of 500 with pests and 500 without pests. 
The accuracy of the experiment A ? CNN from Scratch is 48.33%, precision 1, recall 0.48, 
F1-score 0.65 results in an underfitting model, experiment B ? CNN from Scratch is 
73.00% precision 1, recall 0.64, F1- score 0.78 results in an overfitting model, the C?
CNN from Scratch experiment is 92.00% precision 0.88, recall 0.96, F1-score 0.92 
results in an overfitting model, the D ? CNN add model VGG16 experiment is 95.00%, 
precision 0.91 , recall 0.98, F1-score 0.95 results in a usable model, experiment E ? CNN 
add Xception model 97.00%, precision 0.96, recall 0.98, F1-score 0.97 results produce a 
usable model and experiment F ? CNN add NASNetMobile model of 93.00%, precision 
0.91, recall 0.93, F1-score 0.92 results in a model that can be used. Of the 6 trials, 
experiment A ? CNN from Scratch experienced underfitting, experiment B ? CNN from 
Scratch and experiment C ? CNN from Scratch experienced overfitting. So that the models 
that can be used for the detection process are the D ? CNN add model VGG16 experiment, and the E ? CNN add Xception experiment for large-scale architecture. As for the 
application of the model on mobile devices, a model has been proposed in the F ? CNN 
experiment add the NASNetMobile model to adjust the capabilities of the device.}
		}