The increase in population and economic growth in Sleman Regency causes an increase in the need for land even though in theory the area of ??places does not increase. In order to meet the needs of the land, many land use conversions occur. This study aims to determine land use changes, predict the accuration of land use in Sleman Regency using Artificial Neural Network (ANN) and Cellular Automata (CA) models. The model builder on ArcMap is carried out for the preparation of spatial data before further analysis is carried out using QuantumGIS with CA-ANN analysis to predict land use changes and reclassify method in ArcMAp to test the accuracy of changes in 2019 with existing ones. The data used in the form of spatial data on land use in 2015 and 2017 as well as supporting data or variables in the form of road spatial data and the distribution of educational places. The results showed that there was a considerable increase in the amount of 287,342 Ha, while the use of land as paddy fields actually decreased by 291,93 Ha. Modeling with CA-ANN shows very strong accuracy results, namely the Kappa Index of .95621 and correction of 97.14082%. Results of the prediction conformity with the existing land use shows a suitable percentage of 93,52%.   Peningkatan jumlah penduduk dan pertumbuhan perekonomian di Kabupaten Sleman menyebabkan meningkatnya kebutuhan lahan padahal secara teori menyatakan bahwa luas suatu daerah tidak mengalami pertambahan. Demi memenuhi kebutuhan lahan tersebut banyak terjadi konversi penggunaan lahan. Penelitian ini bertujuan untuk memprediksi dan melakukan akurasi kesesuaian perubahan penggunaan  lahan  di  Kabupaten Sleman  menggunakan  model Artificial  Neural  Network (ANN)  dan Cellular Automata (CA). Model builder pada ArcMap dilakukan untuk persiapan data spasial sebelum dilakukan analisis data lanjutan menggunakan QuantumGIS dengan analisis CA-ANN untuk memprediksikan perubahan penggunaan lahan dan reclassify dalam ArcMap untuk menguji akurasi perubahan di tahun 2019 dengan eksisting yang ada. Data yang digunakan berupa data spasial penggunaan lahan di tahun 2015 dan 2017 serta data atau variabel pendukung berupa data spasial jalan dan sebaran tempat pendidikan. Hasil penelitian menunjukkan adanya pertambahan penggunaan lahan sebagai pemukiman yang cukup banyak yakni sebesar 287,342 Ha sedangkan penggunaan lahan sebagai sawah justru mengalami penurunan luas sebesar 291,93 Ha. Pemodelan dengan CA-ANN menunjukkan hasil akurasi yang sangat kuat yakni pada Indeks Kappa sebesar 0,95621 dan koreksi sebesar 97,14082%. Hasil kesesuaian prediksi dengan eksisting penggunaan lahan menunjukkan persentase sesuai sebesar 93,52%.

The development of Geographic Information Systems (GIS) is able to create future value in various sectors and become a solution to the problem of limitations and disparity of electricity resources in Indonesia. This condition encourages GIS to be an analytical solution to the problem of electricity resources, which is by utilizing solar radiation as a source of renewable energy. This study aimed to optimize GIS in the use of solar radiation on the slope of building roofs which affects the estimated number and average electric power. This study used the mixed method. Research data includes aerial photos, which were analyzed digitally using the area of solar radiation and the slope angle of building roofs so as to produce a spatial analysis of the utilization of solar panels on Derawan Island. The data analysis showed that buildings in Derawan Island can produce 17,355.254 mWh per year with each building producing an average of 28,686 kWh annually. The result of the study is expected to encourage the realization of the use of renewable energy as part of the SDGs by utilizing solar panels as a source of electricity, replacing fossil-derived energy. This study is also expected to be applied in other small inhabited islands to support the sustainability of electricity use and increase the use of renewable energy.