%A NIM.: 18106050044 Zaini Miftah Prasetyahadi
%O Pembimbing: Nurochman, S.Kom., M.Kom.
%T PENERAPAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DALAM MENGIDENTIFIKASI LAHAN KOSONG KABUPATEN KULON PROGO BERBASIS CITRA GOOGLE EARTH
%X Kulon Progo faces several challenges related to its land management.
There have been significant changes in land use over time, including
increased development in previously open areas or even on land that was
previously considered neglected. These changes put pressure on land
availability and create a need for more detailed and effective land
management. In this context, land classification in Kulon Progo is important
because it provides a deeper understanding of land changes and helps in
planning more efficient and sustainable land use.
In this study, the Convolutional Neural Network method was used to
classify land in Kulon Progo, which consists of open empty land, open nonempty
land, and closed land. The dataset was created using images taken
from Google Earth, totaling 5480 images cropped to a size of 50 x 50 pixels.
Before the model was trained, the dataset was processed through
preprocessing stages including cropping, using RGB images, and then
augmentation. The algorithm was then trained with 500 epochs, learning rates
of 0.001, 0.0001, and 0.00001, and Adam and RMSprop optimizers.
From the experimentation that has been done with certain
parameters, the highest training accuracy obtained was 0.9807, produced with
parameters: learning rate 0.0001, epoch 500, and optimizer Adam.
Meanwhile, the highest validation accuracy obtained was 0.8608, produced
with parameters: learning rate 0.0001, epoch 500, and optimizer RMSprop.
%K Lahan Kosong, Google Earth, Convolutional Neural Network,
Accuracy, Training, Validation
%D 2023
%I UIN SUNAN KALIJAGA YOGYAKARTA
%L digilib63098