%0 Thesis
%9 Skripsi
%A Zaini Miftah Prasetyahadi, NIM.: 18106050044
%B FAKULTAS SAINS DAN TEKNOLOGI
%D 2023
%F digilib:63098
%I UIN SUNAN KALIJAGA YOGYAKARTA
%K Lahan Kosong, Google Earth, Convolutional Neural Network,  Accuracy, Training, Validation
%P 86
%T PENERAPAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DALAM MENGIDENTIFIKASI LAHAN KOSONG KABUPATEN KULON PROGO BERBASIS CITRA GOOGLE EARTH
%U https://digilib.uin-suka.ac.id/id/eprint/63098/
%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.
%Z Pembimbing: Nurochman, S.Kom., M.Kom.