Indonesia is a country that has natural geological disasters. One of the most frequent geological disasters in Indonesia is a landslide. Landslides are movements of rock or soil mass due to gravitational forces pulling down accompanied by a driving force on the slopes greater than the innate material. Identification and mapping of landslide potential areas has an important role as an effort to overcome and anticipate landslides. One of methods that can be used to determine potential landslides is the SMORPH method. The SMORPH method will produce a landslide classification based on a matrix between slope angle and slope form. This study aims to analyze the landslides potential distribution area in Kebumen Regency. Landslide identification was carried out at 30 locations of landslide events. Spatial analysis of landslide potential is done by overlaying the slope angle with slope form. The potential level for low landslides has a percentage of 79.54% in the southern and central parts of Kebumen Regency, the level of moderate potential level with a percentage of 8.81% randomly scattered over the Kebumen Regency, and 11.65% of high potential level in the north and southwest of Kebumen Regency.

Development and economic growth in an area can cause land cover changes. Penajam Paser Utara Regency, as a new capital candidate, is also predicted to experience in land cover changes. Land cover changes that are not following the land’s potential will cause environmental problems, so it is necessary to predict land cover changes by looking at patterns of land cover changes in the past and the factors that influence it. The purpose of this study is to analyze and predict the land cover change in Penajam Paser Utara Regency in 2031. The method used in this study is modeling using Cellular Automata - Markov. The driving factor of land cover change is used in making prediction models such as distance from the center of activity, distance from the road, distance from the river, elevation, and slope. The prediction land cover changes show that there has been an increase in plantation area and a decrease in forest area, while the development of the built-up area is not visible. The kappa test for predicted land cover showed perfect results. The resulting land cover model can be used to formulate land-use policies.

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The coastal area has experienced significant changes of waste problems over the past few years. To resolve the waste problems in coastal areas, an understanding of community perception is needed to support government efforts. Therefore, this study aims to review people’s perspectives on the dynamics of waste in the coastal areas. Community perception data were compiled through semi-structured interviews with the surrounding communities in coastal areas. ArcGIS and load count analysis were used to analyze the waste density. Waste was collected from the coastal area in Ambon Bay and analyzed using waste density calculation and spatial analysis. The results show that the total waste density obtained at the coastal area of Ambon Bay is 0.249 kg/m2, of which 0.078 kg/m2 is the density of plastic waste, and 0.171 kg/m2 is the density of non-plastic waste. Communities in coastal areas have made efforts to deal with waste problems, but the efforts made are still ineffective in overcoming these problems. That problem happens because there is a lack of knowledge of the community and lack of infrastructure in coastal areas. The research results have the potential for replication in other coastal areas and are used as the basis of decision making for waste management improvement.

The increase of population could leads to residential area availability. This could lead to imbalance between population and available housing and could results in higher population pressure on the available area. Spatial modeling prediction is needed as a prevention step to prevent excessive land cover change in the future. This research aims to analyze residential area carrying capacity and spatial modelling of land cover change of Samarinda in 2006, 2014 and 2020 using Cellular Automata Markov Chain (CAMC) and residential area carrying capacity index. The Cellular Automata Markov Chain (CAMC) results show that there is an expansion of residential area land cover which affected by driving factors that consist of distance from the nearby road, distance from the river, distance from the point of interest (health facility and education facility), slope, and elevation. Residential area land carrying capacity affected by population density, standard needed land area, and residential area extent in Samarinda City. Thus, it is needed to analyze the model to see the available area for residential area development sustainably.