The phenomenon of landuse change from an undeveloped area into a built-up area is often the case, especially in big cities. Population growth, both in birth and migration rates, is one of the factors that causes the need for land for various human activities. Tendency for landuse change is expected to continue in the following years along with a region development. The city of Makassar has a tendency for landuse change. This is due to the position of Makassar as the capital of the South Sulawesi province which has A-level public service and it has become a separate magnet for people from outside the city to conduct activities and live in the city. The purpose of this research is to predict landuse/landcover (LULC) change until 2033 by classifying using Landsat satellite imagery include 2008, 2013, and 2018 into 5 landuse/landcover classes in Tamalanrea Sub-District with the Modules for Land Use Change Simulations (MOLUSCE): Multi-Layer Perceptron Neural Network and Geographic Information System method. This research shows the percentage of changes in 5 classes of landuse from 2018 to 2033, are: agriculture area with -0,30%; built-up area with 3.15%; barren area with -5.11%; vegetation with 0.98%; and water body with 1.27%.
Gorontalo is located at the macro and micro plate boundary, therefore it is located in an active seismotectonic region. This study aims to analyze earthquake damage level in Gorontalo based on seismicity and peak ground acceleration. The data used is obtained from the USGS. Data is made into a database and plotted onto a geological map. Calculation of peak ground acceleration is obtained using the Kawashumi formula. The results of this study indicate that Gorontalo is included in the slight to moderate earthquake damage level because it is dominated by shallow to intermediate earthquake's depth, light to moderate earthquake magnitude, and have a peak ground acceleration 1,462-99,714 gal.
The spatial plan program for Makassar City and the surrounding area called Mamminasata (Makassar, Maros, Sungguminasa, and Takalar) was created by the Indonesian Government. The program regulates the proportion of land cover, but predictions about land cover changes were not considered. Therefore, in this study, we predict what the land cover may be in 2031 using the multi-layer perceptron neural network and the Markov chain methods. For this purpose, image composite, support vector machine classifier, and change detection were applied to a time series of satellite data. Visual validation showed the hot-spots of land cover changes related to population density, and statistical validation scored 0.99 and 0.78 in no information kappa and grid-cell level location kappa, respectively. The model was performed to predict land cover in 2031, and the predicted result was then compared with the spatial plan using an overlapping method. The results showed that built-up area, dryland agriculture, and wetland agriculture occupied two, twenty, and eight percent of the protected zone, respectively. Meanwhile, fifteen percent of the development zone was covered by forest, mainly in the eastern part of Mamminasata. The result can be used to help the Government decide future plans for the Mamminasata area.
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