Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Nazalia, Cendekia Luthfieta
Palupiningsih, Pritasari
Prayitno, Budi
Subject
Teknik Informatika
Datestamp
2023-06-20 08:02:30
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.