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On November 21, 2022, the Cianjur area in West Java, Indonesia, experienced devastating landslides caused by a magnitude 5.6 earthquake, with the epicenter traced to a hidden active fault approximately 11 kilometers beneath the surface, known as the Cugenang Fault. This study investigates the post-disaster landslides and aims to discover the characteristics of the landslides triggered by this earthquake. The methodology involves literature review, lithology descriptions, landslide slip surface analysis, soil analysis, slope assessment, land cover and land use change examination, landslide orientation evaluation, and distance measurements to the earthquake's epicenter and the Cugenang fault. Drones aided spatial landslide analysis. The study highlights the strong link between intense shaking along the active fault and landslide occurrences. Due to lithological bedding control, these landslides all head southwest and are located 0.35 to 0.67 kilometres from the fault. The residual soil, highly weathered breccia, and possible tuff volcanic deposits, which act as a sliding plane, lie in a similar direction to the slope, creating a vulnerable setting for landslides. The morphology and exposures of the Sarampad and Rawacina landslides showed similar phenomena to liquefaction. Land use changes, including slope cutting without proper stabilization measures, contribute to landslides to a certain degree.

Java Island holds the highest record of landslide events in Indonesia. In 2021, the Bogor area, consisting of the city and regency of Bogor, recorded the highest number of landslides. These events further impact the fatalities, damage, and loss to society. Landslide mitigation should be considered to reduce the risk caused by landslide hazards. In this regard, a landslide susceptibility analysis is one of the fundamental steps in mitigation measures that can support policymakers in response to landslide disaster risk reduction. The location of landslide possibilities can be identified by mapping landslide susceptibility. Therefore, this study aims to produce a landslide susceptibility map (LSM) using a statistical frequency ratio method and logistic regression. The number of landslide inventories used in the model is about 822 events. To apply the model, the present study evaluates 13 influencing factors consisting of elevation, slope angle, slope aspect, slope curvature, Topographic Wetness Index (TWI), distance to river, lithological, distance to fault, soil type, annual rainfall, Normalized Difference Vegetation Index (NDVI), land use land cover (LULC), and road distance. The model performance is further evaluated using Area Under the ROC Curve (AUC). The frequency ratio (FR) and logistic regression (LR) models produce satisfactory results and have high predictions of future landslide occurrences with a score of 0.8317 and 0.8817, respectively. [ABSTRACT FROM AUTHOR]