Slope failure is a complex event. It can provide useful information about the condition of soil parameters on the failed slope in the same way it can provide an opportunity to evaluate the stability of other slopes. To evaluate the occurence of slope failure, unit weight data and shear strength properties of soil are needed, as well as methods of analysis including failure mechanisms. One of the methods used to evaluate landslide events is reverse analysis. In this study, reverse analysis was carried out on landslides that occurred on the slopes of D-D 'and F-F' at the Integrated Campus Building of the Institut Teknologi Kalimantan. The finite element method is used to analyze the safety number of the slopes under review. From the results of the reverse analysis, it was obtained that the soil parameters at the time of collapse in the top layer resulted in the value of unit weight (γ) = 20 kN / m2, Cohesion (c) = 2 kPa and Internal friction angle (φ) = 27 ° . Slope failure occured due to an increase in soil volume weight value, as well as a decrease in soil shear strength parameters, namely cohesion and internal friction angle.

There have been many attempts and methods for predicting landslide-affected areas; empirical methods, numerical methods, and laboratory models are commonly used for prediction. Laboratory and numerical models require an input of parameters that are difficult to determine accurately. At the same time, empirical statistical methods use statistical methods based on historical data of landslide events to form an empirical model. Statistical analysis of empirical observations builds a possible relationship between disaster area characteristics and slide behavior because it does not require detailed mechanics of avalanche movement; the empirical-statistical model is a simple and practical tool in the initial assessment to predict the sliding distance of an avalanche that will occur. The main discussion of this study is that the volume of avalanches (V) has a more significant influence than the height of the slope (H) on the length of the avalanche (L) that occurs. Fifty-nine data on landslide events that have occurred in Indonesia are used to a prediction model for landslide events reviewing the slope geometry parameters in the form of H, slope (θ), and V and discussing the main factors that affect the sliding distance of avalanches that have not been discussed in research in the Indonesian territory. The analysis shows that H has a significant effect on the sliding distance of the avalanche compared to V. The best model produced to predict the sliding distance of the avalanche is L = 6.918 H0,840 and produces an average error rate of 29% for the landslide measurement data.

Slope stability analysis was carried out in the Institut Teknologi Kalimantan which is an area with educational facilities that have a high risk of soil movement. The research area consists of slopes that often reported landslides, especially during the rains time. Evaluation is carried out to the important factors that affect slope stability such as slope geometry consist of 23 zones, soil parameters consist of 7 drilling holes, and past landslide occurrence. Slope stability was evaluated using back analysis based on soil parameters under critical conditions and field investigation and slope geometry using SLOPE/W software to obtain the safety factor (SF) of a certain slope. Landslide vulnerability maps are created based on the risk and potential landslide or landslide occurrence. Based on the results of the stability analysis, there are 6 zones with unstable conditions and a high potential for landslides and 4 zones in critical conditions with moderate landslide potential. The modeling also shows that in the same soil parameter value with a steep slope, the value of the safety factor is getting smaller. This means that the potential for soil movement is greater. Besides, the loading at the top of the slope also causes a reduced safety factor.

Prediction of bearing capacity and soil type is a requirement for the safety of construction before planning a building construction. Therefore, it is necessary to research the distribution of bearing capacity and conical resistance to determine soil conditions in an area. Based on CPT data, this study mapped cone resistance distribution and soil-bearing capacity distribution in the ITK masterplan area. The analysis was carried out by reviewing the 40 cm pile foundation at 11 m. The analysis was carried out using the Kriging, IDW, and Spline with Barriers methods. The bearing capacity was analyzed using the Trofimankove method. The mapping of the carrying capacity using the three interpolation methods results in a Qall value of around 26,024 – 87,835 tons. The cone resistance mapping results using the three interpolation methods show that the soil consistent in the ITK masterplan area is stiff, very stiff and Hard with a qc value of around 16,0804 – 259,54 kg/cm2. The ITK masterplan area has a type of foundation soil, which is hard. The comparison results obtained from mapping the cone resistance and the carrying capacity of the three interpolation methods used, the value of the range of qc and Qall closest to the sample data used is the IDW method

landslide disaster. Based on this fact, a method is needed to predict the range of landslides to minimize the impact of disaster losses. The empirical statistical method is one of the methods that can be used to predict landslides by taking input data from the history of previous landslide events. This research aims to find the best modeling form for sliding distance prediction and which parameters influence a landslide's sliding distance prediction. This study used multiple linear regression methods. The data used in this study are geometric slope parameters in the form of slope height (H), original slope (θ), landslide area (A), and rock type (RT). The data was taken from the 2015-2021 PVMBG landslide investigation report and used the Google Earth and Global Mapper program. Based on the analysis of the best empirical model that can predict the sliding distance of a landslide log Lmax = 0,387 – 0,097 RT + 0,230 log H + 0,458 log A – 0,220 tan θ with an R2 value of 0,94 and an average estimated error of 31,56%. The parameter that has the most influence on the prediction of sliding distance is the area affected by the landslide (A).